# Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors

arXiv: https://arxiv.org/abs/2507.21989 · HTML: https://mlsanity.com/paper/2507.21989/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Vector search and ANN indexes, one of 15 directions in Retrieval-augmented generation.
- Builds on 34 of its 143 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Placed in

- Retrieval-augmented generation / [Vector search and ANN indexes](https://mlsanity.com/rag/vector-search-and-ann-indexes/)

## Builds on (most cited first, 30 of 34)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://mlsanity.com/paper/2405.17428/) (2024)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [RedCaps: web-curated image-text data created by the people, for the people](https://mlsanity.com/paper/2111.11431/) (2021)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [Jasper and Stella: distillation of SOTA embedding models](https://mlsanity.com/paper/2412.19048/) (2024)
- [Practical and Optimal LSH for Angular Distance](https://mlsanity.com/paper/1509.02897/) (2015)
- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2405.12497/) (2024)
- [Optimal Data-Dependent Hashing for Approximate Near Neighbors](https://mlsanity.com/paper/1501.01062/) (2015)
- [FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search](https://mlsanity.com/paper/2105.09613/) (2021)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Qd-tree: Learning Data Layouts for Big Data Analytics](https://mlsanity.com/paper/2004.10898/) (2020)
- [The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform](https://mlsanity.com/paper/1908.07389/) (2019)
- [Approximate Nearest Neighbor Search with Window Filters](https://mlsanity.com/paper/2402.00943/) (2024)
- [CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks](https://mlsanity.com/paper/2406.02524/) (2024)
- [UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors Search](https://mlsanity.com/paper/2412.02448/) (2024)
- [Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2503.15191/) (2025)
- [HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints](https://mlsanity.com/paper/2207.07940/) (2022)
- [CAPS: A Practical Partition Index for Filtered Similarity Search](https://mlsanity.com/paper/2308.15014/) (2023)
- [Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints](https://mlsanity.com/paper/2203.13601/) (2022)
- [Enhancing Lexicon-Based Text Embeddings with Large Language Models](https://mlsanity.com/paper/2501.09749/) (2025)
- [Reconfigurable Inverted Index](https://mlsanity.com/paper/1808.03969/) (2018)
- [Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data](https://mlsanity.com/paper/2505.06501/) (2025)
- [iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search](https://mlsanity.com/paper/2409.02571/) (2024)

## Built on by (most cited first, 2 of 2)

- [Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis](https://mlsanity.com/paper/2602.11443/) (2026)
- [Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions](https://mlsanity.com/paper/2607.01283/) (2026)

## Abstract (arXiv)

Advances in embedding models for text, image, audio, and video drive progress across multiple domains, including retrieval-augmented generation, recommendation systems, and others. Many of these applications require an efficient method to retrieve items that are close to a given query in the embedding space while satisfying a filter condition based on the item's attributes, a problem known as filtered approximate nearest neighbor search (FANNS). By performing an in-depth literature analysis on FANNS, we identify a key gap in the research landscape: publicly available datasets with embedding vectors from state-of-the-art transformer-based text embedding models that contain abundant real-world attributes covering a broad spectrum of attribute types and value distributions. To fill this gap, we introduce the arxiv-for-fanns dataset of transformer-based embedding vectors for the abstracts of over 2.7 million arXiv papers, enriched with 11 real-world attributes such as authors and categories. We benchmark eleven different FANNS methods on our new dataset to evaluate their performance across different filter types, numbers of retrieved neighbors, dataset scales, and query selectivities. We distill our findings into eight key observations that guide users in selecting the most suitable FANNS method for their specific use cases.