Where Vector Search Shines: Why Not All Use Cases Are Equal
In my last post, I discussed the fundamental problems of vector search for text-based information retrieval. But after numerous conversations with colleagues and some reflection, I need to add an important nuance: Not all vector search applications suffer from the same problems. In fact, there are areas where vector search doesn’t just work well – it truly excels.
Let me explain why image search is the perfect example of where vector search can fully demonstrate its strengths – and what that teaches us about the nature of the underlying problem.
The Fundamental Issue: Certainty vs. Similarity
The crucial difference lies in the nature of what we’re searching for. In text-based information retrieval (like in RAG systems), we’re looking for certainty and completeness. We want to find all relevant facts, not miss anything important, and provide definitive answers.
In image search, however, we’re looking for similarity and inspiration. We’re not asking: “Is there somewhere an image of exactly this sunset?” We’re asking: “Show me images that are similar to this sunset.”
That’s a fundamental difference in problem definition, and it makes vector search the ideal solution.
Why Image Search Works Perfectly with Vector Search
1. The Single-Query Advantage: A Picture Is Worth a Thousand Words (and Avoids Multi-Semantics)
Remember the Multi-Semantic Query Paradox from my last post? When searching text for “sustainable energy investment opportunities in developing countries,” we have to navigate four different semantic domains simultaneously.
In image search, the input image itself is already a complete, unambiguous representation of what we’re looking for. When I search for images similar to my photo of a misty mountain forest, there’s no ambiguity about which visual properties matter – they’re all encoded in the image:
- The color palette (green-blue tones, muted colors)
- The composition (vertical lines of trees, depth effect through fog)
- The texture (organic, natural, soft through fog)
- The mood (calm, mystical, melancholic)
The embedding model doesn’t have to guess which of these aspects is more important – it can process them all simultaneously because they all exist in the same visual modality. There’s no “prioritization problem” because the priorities are given by the visual composition itself.
2. Top-K Is Exactly What We Want
In information retrieval for RAG, Top-K (returning the K most similar documents) is a pragmatic limitation. We’d actually like to have all relevant documents but have to limit ourselves to the best K for practical reasons. The completeness problem arises because we never know if the K+1-th document contains critical information.
In image search, however, Top-K is exactly the desired functionality. Nobody wants to see all 10 million somewhat similar images in a database. You want the 20-50 most visually similar images to:
- Find inspiration for a design project
- Identify duplicates or similar photos
- Discover visually related products
- Explore a style direction
The “problem” that we don’t know if we’ve found everything isn’t a problem here – it’s a feature. The fact that somewhere in the database another image exists that’s 65% similar, while we already have 50 images with 85%+ similarity, is completely irrelevant.
3. The Absence of the HyDE Multiplication Problem
For image search, we don’t need HyDE or similar techniques at all. We already have the “hypothetical” document – it’s the input image itself. There’s no need to generate multiple semantic interpretations because the visual modality is already complete and unambiguous.
When I use an image of a red sports car as a query, the system doesn’t have to guess whether I’m:
- Searching for the color
- Searching for the vehicle type
- Searching for the angle or perspective
- Searching for the lighting mood
All this information is simultaneously and equally present in the image. The embedding model can capture it all without having to make an impossible choice.
4. Fuzzy Matches Are Desired, Not Feared
In text search, fuzziness is often a necessary evil. We actually want exact matches of facts and concepts but have to work with semantic similarity because different texts express the same ideas differently.
In image search, fuzziness is exactly what we want. When I search for images similar to my beach photo, I don’t just want exact duplicates – I want:
- Other beaches with similar lighting mood
- Coastal landscapes with comparable composition
- Images that have the same emotional impact
- Photos with a similar color palette but perhaps different subjects
The “fuzziness” of vector search becomes a strength because it enables exactly this kind of creative, surprising similarity that’s so valuable in image exploration.
5. The High-Risk Problem Doesn’t Exist
In RAG systems for medical diagnoses or legal research, incomplete information retrieval is potentially dangerous. An overlooked case study or an unfound precedent can have serious consequences.
In image search, the risks are dramatically lower:
- If you’re searching for a similar product photo and a few relevant results are overlooked, that’s annoying but not catastrophic
- If you’re seeking design inspiration and don’t see every potentially relevant image, you still have enough material to work with
- If you’re identifying duplicate photos and miss some, you can simply repeat the search with different parameters
The error tolerance is orders of magnitude higher.
Other Use Cases Where Vector Search Excels
These principles don’t just apply to image search but to an entire class of use cases:
Music Similarity
“Find me songs that sound like this one” – again we have a single, complete query (the song), we’re searching for similarity instead of completeness, and fuzzy matches are desired.
Product Recommendations
“Show me products similar to this one” – users expect a curated selection, not an exhaustive list. Top-K is perfect.
Content Discovery
“More like this” in streaming services – users want surprise and variety, not absolute completeness. The fact that somewhere in the database there’s another movie that’s a 63% match, while you already have 20 options with 80%+ match, is irrelevant.
Facial Recognition and Biometric Search
“Find images of this person” – a single visual query where Top-K candidates for human verification is exactly what we need.
The Common Pattern: Subjective Similarity vs. Objective Completeness
What all these successful use cases have in common:
- Single-Modal Query: The query is in the same modality as the documents (Image→Image, Audio→Audio)
- Inherent Top-K Nature: We naturally want only a manageable number of similar results
- Similarity Is the Goal: We’re not searching for “all relevant” things, but for “the most similar” things
- Low Error Consequences: Overlooked results are annoying, not dangerous
- Subjective Evaluation: The user can decide for themselves whether the results are “good enough”
This stands in stark contrast to text-based information retrieval, where:
- Multi-Semantic Queries: Text queries often contain multiple mixed concepts
- Completeness Is Critical: We must find all relevant information
- Facts Are the Goal: We’re searching for objective truth, not subjective similarity
- High Error Consequences: Overlooked information can lead to false conclusions
- Objective Evaluation: There’s a “right” and “wrong,” not just “similar enough”
What This Means for System Design
The insight here isn’t that vector search is “good” or “bad” – it’s contextually optimal. As developers, we need to ask ourselves:
Vector Search Suitability Checklist:
- Is the query in the same modality as the documents?
- Are users searching for similarity rather than completeness?
- Is Top-K a natural answer to the question?
- Can users subjectively evaluate the quality of results?
- Are the consequences of overlooked results acceptable?
If you can answer most of these questions with “Yes,” vector search is probably an excellent choice. If you answer them with “No,” you should take the limitations seriously and consider hybrid approaches.
Conclusion: The Right Tool for the Right Job
Vector search isn’t a universal solution, but it’s also not a flawed tool. It’s a specialized tool that works brilliantly for certain problem classes.
In image search, music recommendations, and similar applications, vector search demonstrates its strengths:
- Single-modal queries avoid multi-semantic problems
- Top-K is exactly the desired behavior
- Fuzzy matches create value instead of uncertainty
- Low error risks make incompleteness acceptable
In text-based information retrieval for high-risk applications, we should take the limitations seriously and be appropriately cautious.
Wisdom lies not in glorifying or condemning vector search, but in understanding when its strengths match our problem – and when they don’t.