Drawing ligand selections from a variety of models built on different representations was expected to help active learning more uniformly sample the chemical space in the early iterations. However, since only the best predicted binders were selected from every model, the narrowing scheme performs similarly to the simple greedy protocol which relies on a single ligand representation (Fig. ). Both approaches are efficient at quickly exploring a narrow branch of the chemical space to identify potent binders. Interestingly, the uncertaintydriven protocol performs well in an overall description of the chemical library. It sacrifices the ability to quickly identify high affinity binders, but includes a broad range of ligands in the training set, thus providing more accurate global description of the ligand set. For future investigations, it might be a promising avenue to combine uncertain and greedy protocols either into a narrowing-style scheme, where the first few iterations are handled with the uncertain selection rule and later iterations by the greedy one, or a modified mixed scheme, where the ratio of the most uncertain to most strongly binding predictions is changed at each iteration to smoothly transition from the uncertain to greedy selection during active learning.