Varying network depths and layer widths were probed. Preliminary hyper-parameter optimization of these values was carried out on the retrospectively analyzed dataset in Part I. The resulting values were used for the first three iterations of active learning on the prospective dataset in Part II. In iteration 4, hyperparameters were reoptimized and feature selection was performed by selecting the best combination of previously described ligand encodings to use for the available training data. The combination of 2D_3D descriptors and PLEC fingerprints performed the best. In addition, for this iteration feature selection was performed by discarding the features whose mean importance determined by Integrated Gradients 67 was under 0.02. Subsequent iterations used the full 2D_3D ligand representation without further feature selection. The details of the metaparameters used with the prospective dataset are in Table . Active learning on the retrospective dataset reused many of the hyperparameter values from the corresponding iterations of the prospective case (Table ).