Another selection strategy adopted in workflows WF1456 and WF1454 was to dock a small and diverse library with GNINA or Vina 20, respectively, to initiate iterative active learning cycles where a ML model is trained on a small set of docking scores to predict ML-scores for billions of commercial compounds, and where ML-scores are used to select the next small subset for docking and refinement of the ML model. In WF1454, the selection was further refined with a round of consensus scoring.