We developed a molecular HNSW using the open-source library hnswlib, albeit with two primary adaptations for molecular fingerprint processing: Tanimoto distance calculations for the HNSW construction distance metric 40 and integer data types for memory-efficient fingerprint storage and expedited Tanimoto calculations. Using rdkit, we converted the chemical library into Morgan fingerprints with a radius of 2 and a length of 1024 bits. We constructed the HNSW with an efConstruction of 400 and M of 16. We chose fingerprints and HNSW parameters that demonstrated the best average recall of DUDE-Z virtual actives when applying RAD to screen up to 10% of the chemical library (Supplementary Figure and Supplementary Table ). While these parameters yielded the best recall of virtual actives in this context, we note that many other parameter choices resulted in similar performance while reducing HNSW construction speed and memory usage (Supplementary Figure ). Finally, we converted the flat HNSW C++ data structure produced by hnswlib into a series of Python graph-tool graphs for easier traversal.