Our models show higher correlation with experimental RBFE and lower errors of prediction than the model developed by Jiménez-Luna et al. when evaluated on the additional ligands dataset. We see an increase in model performance as the amount of information about each congeneric series is increased. Our models do not show diminishing returns as more ligands are added to the training set, unlike the model developed by Jiménez-Luna et al. . Our highest parameter CNN architecture, Dense, was able to outperform the lower parameter Default2018 architecture on the smallest training set. However, the Dense model is initialized with weights from an absolute binding affinity prediction task that provide the model with much greater initial knowledge of the problem than a randomly initialized network. When using the Dense architecture with random initialization, the model had lower RBFE prediction performance than the randomly initialized Default2018 model (results not shown). The Dense model was unable to train effectively without L 2 regularization (weight decay), likely due to the small amount of data and the large amount of parameters in the model.