Eventually, though, after the majority of the strongly binding ligands have already been identified and added to the training set, their pool is exhausted. With the decrease in number of strong yet unidentified binders, the models begin to back-fill the training set with weaker binding ligands: the strongest identified binders at iterations 5 and 6 do not out compete the best binders identified earlier (inset in panel D of Fig. ). As the probability of finding even higher affinity ligands now drops with each iteration while the computational cost remains the same, about 5 iterations appears to be optimal for halting the active learning process.