(1) Although ML predictions are faster than direct docking calculations, the necessity for repeated cycles of active learning introduces significant computational overhead. Overhead includes repeated model training and inference across the entire library, which is costly, particularly for ultra-large chemical libraries containing tens of billions of molecules. While strategies like design space pruning aim to minimize the number of ML predictions required in successive active learning iterations, an initial comprehensive library evaluation is unavoidable. Furthermore, each active learning cycle requires model training and validation, contributing to the overall computational overhead.