We propose PipeANN-Filter, an efficient filtered vector search system on SSD. Unlike existing systems that explore only valid vectors (i.e., those satisfying the attribute constraints) during search, PipeANN-Filter explores a superset of valid vectors, and performs attribute verification after getting the top-k closest result vectors. This allows PipeANN-Filter to leverage probabilistic data structures (e.g., Bloom filters) to identify the superset, trading off a small number of false-positive vector explorations for a massive reduction in SSD I/O for attribute reading. Evaluations show that PipeANN-Filter improves search latency and throughput compared to state-of-the-art systems. PipeANN-Filter is open-source at https://github.com/thustorage/PipeANN
Cite
@article{arxiv.2605.17992,
title = {PipeANN-Filter: An Efficient Filtered Vector Search System on SSD},
author = {Hao Guo and Jiwu Shu and Youyou Lu},
journal= {arXiv preprint arXiv:2605.17992},
year = {2026}
}