Because the HNSW uses Morgan fingerprints and Tanimoto distance, neighbors within the graph have similar fingerprints and are more likely to occupy the same Butina cluster. Despite the sparse upper layers of the HNSW facilitating the exploration of distant regions of chemical space, traversal is inherently constrained to neighbors of good-scoring molecules, potentially impeding the exploration of some clusters. Different representations capture distinct molecular information, influencing the chemotypes considered similar and demonstrating variable performance on downstream biological tasks. We anticipate advancements in these representations, such as those emerging from machine learning, will improve the recall rate and diversity of virtual actives identified through RAD.