Altering the Siamese network architecture does not affect performance as much as removing components of the loss function. If we exchange the latent space subtraction of the Siamese network for a concatenation, we do not see any change in performance of the model for RBFE prediction. However, we find that when training the Siamese network with latent space subtraction on only one ordering of each ligand pair, the network is better able to comprehend the negation of the ∆∆G when the ligand ordering is reversed (Table ). This demonstrates that the latent space subtraction better embeds the symmetry of the ∆∆G prediction problem in comparison to latent space concatenation. We train a single-arm convolutional architecture to predict the absolute affinity values using the same training set (No Siamese Network in Table ). The single-arm convolutional architecture's absolute affinity predictions are subtracted for pairs of ligands to produce RBFE predictions. The single-arm convolutional architecture is worse than the CNN Siamese network at both absolute and relative binding affinity prediction.