RAD efficiently recovers a significant proportion of the best-scoring molecules (virtual actives) within the entire chemical library while only evaluating a fraction of the total library. Moreover, this method exhibits comparable performance to the active learning method presented in Yang et al. while addressing some limitations: (1) RAD exploits the intrinsic graph structure of HNSW, necessitating score calculations for only a subset of the library. This is a notable improvement over the active learning approach, which requires ML predictions for the entire library, potentially multiple times. We use explicit docking calculations as the scoring function during traversal. Nonetheless, the HNSW structure can be traversed with any scoring function, such as an ML-based scoring function, or even be integrated within an active learning framework, eliminating the need for model predictions across the entire library.