We also explored a mixed strategy first proposed by Yang et al . This strategy selects the most uncertain ligands among a larger number of the strongest predicted binders. While the mixed strategy outperforms random ligand selection, in contrast to the finding of Yang et al., it finds desirable ligands slower than the narrowing and greedy approaches. Here, we used a 3:1 ratio of the number of selected predicted strongest binding ligands to the number of selected most uncertain ones among them. In the original work by Yang et al the ratio was 50:1, possibly explaining the observed difference in performance. However, a ratio this large was impractical in our case given the limited size of our dataset of 2351 ligands only. In practice, the performance of this selection approach can be tuned by changing the above ratio, becoming equivalent to the greedy strategy with a 1:1 ratio, i.e. selecting only the strongest binders, and equivalent to the uncertain strategy when the identified strongest binders cover the whole dataset. However, such tuning is impractical in a real prospective study, as it requires rerunning the AL protocol multiple times and finding reference free energies for all the discovered ligands in each repeat to identify the optimal As the protocol progresses and more ligands are added to the training sets, all selection strategies result in better agreement with the predictions from the final iteration, but the uncertain strategy improves correlations the fastest (Fig. ). Overall, the greedy, narrowing and mixed approaches are able to quickly locate the best binding ligands. The greedy and narrowing approaches do so faster and more consistently identify the same high affinity binders (Fig. ). However, the Kendall's rank correlation between the predicted and experimental binding free energies is low for all the selection methods besides the uncertainty-driven one (Fig. ). This results in large numbers of ligands with lower experimental affinities being selected for evaluation and inclusion into the training dataset at each iteration.