From the third iteration the narrowing selection rule behaves exactly as the greedy protocol. The procedure switches to training an ensemble of five models only on the 2D_3D representation. The next 100 ligands are selected based on the mean prediction of these five models, allowing for some cancellation of errors in regions of insufficient training data as a result of this en-semble approach. As active learning continues, it focuses on the three high affinity regions and selects ligands with lower experimental binding free energies than initially (Fig. ). The training sets also become progressively biased towards strongly binding ligands with each iteration (Fig. ). Despite this, the distribution of predicted binding free energies does not change much following the initial iteration (Fig. ).