Having identified the 2D_3D descriptor representation as the most robust molecular encoding we further investigated the performance of ligand selection strategies. In the above analysis we used the greedy strategy of selecting the strongest binders predicted at every iteration. While this leads to rapid improvement of binding affinities, it runs the risk of getting trapped in the first local minimum that is found. To mitigate this risk, we developed the narrowing strategy, where in the first three iterations we instead focus on broadening the scope of exploration in the chemical library and in the later iterations switch to the greedy selection mode. For these first three iterations, we train separate models for all the ligand representations discussed above as well as binned variants of MDenerg and MDenerg_LR, select the five that have the best internal cross-validation RMSE, and use the top 20 ligands predicted by each of them. If multiple representations yield the same ligand in their top 20s, we use the next best ligand from one of the representations, making sure that we select 100 unique ligands for the next iteration. After the third iteration we switch to the greedy approach with the 2D_3D representation to descend deeper into the best local minimum found so far. All in all, this strategy performs similarly to the greedy approach and finds the most potent binders in the library at a comparable rate (Fig.