As discussed, we include with the FEgrow workflow the option to score the output poses of the designed ligands with a scoring function. In particular, we use the gnina convolutional neural network score, which has been trained on both binding pose and affinity prediction. While accurate recovery of experimental binding affinity is not necessarily expected for current scoring functions, it is useful to evaluate to what extent they can be used to provide guidance in early stage design, ahead of more rigorous physics-based scoring methods. The root-mean-square error between gnina CNN affinities (converted to free energies) and experiment is quite acceptable (Table ), ranging from 0.9 kcal/mol (BACE(P2)) to 1.7 kcal/mol (Jnk1), which indicates that the CNN scoring function is able to predict the affinity range of most of these series. In fact, the errors may be lower than typically expected, because we are using here additional information from experiment (the binding pose of the core) and not relying on the scoring function to determine the bioactive conformation.