Consequently, RAD's efficiency becomes pronounced when screening against multiple protein targets. The protein-agnostic HNSW requires a one-time investment in graph construction, eliminating further preparation for new targets. In contrast, the ML models used in active learning must train individually for each protein, with the entire workflow scaling linearly with the number of targets (Figure ). The active learning preparation, repeated per target, incurs multiplicatively higher costs than RAD when screening against numerous targets. Intriguingly, the primary time expenditure across all methods, particularly at the 4.8 billion library scale, remains the docking calculations used to traverse the HNSW or rescore the molecules from active learning or null traversals (Figure , right). It has not escaped our notice that a workflow combining ML scoring with HNSW traversals could serve as an exceedingly rapid albeit approximate heuristic, but this is outside the present scope.