The nature of the chemical space exploration by means of active learning (Fig. ) can be strongly influenced by the strategy of ligand selection for ML model training. The random and uncertain strategies provided a decent general description of the compound library, yet for a task of lead optimization, one might prefer to obtain a better description of the most potent binders. The greedy and narrowing ligand selection approaches rapidly find strongly binding ligands, at the cost of better describing high affinity ligands than low affinity ones. For example, the last iteration of the active learning protocol on the prospective library predicts binding free energies for the ligands with the strongest predicted binding at an RMSE of 1.06 ± 0.14 kcal/mol, while for the weakest pre-Figure : Metrics of model accuracy (A,B) in active learning on the prospective ligand library and a comparison of the predicted and reference binding free energies used in its fourth iteration (C). Predictions for validation ligands are compared to experimental binding affinities, while for all other ligands free energies from simulations are used as the reference. Vertical dotted lines in (A) and (B) represent the switch from using models from multiple ligand representation to a single one. For regression models, true positive rate can only be determined relative to a threshold. Here we use a threshold of -13.29 kcal/mol, equal to the experimental binding free energy of the best binding validation ligand and depicted as dotted lines in panel (C). Grey region indicates absolute unsigned errors of up to 1 kcal/mol. Error bars represent standard errors of the mean and for RMSE and TPR are determined via bootstrapping.