We describe the filtering and usage of the training and evaluation datasets for our RBFE models. The architecture and training hyperparameters of our CNN Siamese network are explained. Our CNN Siamese network is compared to state of the art methods for RBFE prediction on a retrospective lead optimization task and several benchmark datasets. Next, we investigate the relative importance of the components of our model on a small retrospective lead optimization task. Finally, we evaluate our model on novel protein families utilizing a leave-one-family-out cross validation to elucidate the generalizability of our model.