Only some components of the loss function are contributing to the models RBFE prediction performance. The removal of L ∆∆G does not have a large impact on model performance indicating L ∆G and L consistency contribute significantly to the RBFE prediction performance. However, it is important to note the L consistency encourages the model to make the ∆∆G prediction identical to the differences in absolute binding affinity predictions which will provide some notion of ∆∆G loss. The removal of the L rotation component did not significantly change the performance of the model, which may indicate some isotropic properties of the network architecture. The latent space structure imposed by the subtraction operation did not result in improved performance when using both ligand orderings for training. However, training on only one ordering of ligand pairs demonstrates that latent space subtraction is able to embed the RBFE ligand ordering symmetry. This symmetry embedding can be learned by a network using latent space concatenation when using the freely available other orderings during training. The Siamese architecture enables understanding of ordering within congeneric series, which is ignored when only training for absolute affinity prediction. Without the Siamese architecture, the performance of the model suffers on both relative and absolute binding affinity prediction. This may be due to both the symmetry embedding of the network architecture and the increased regularization of the latent space that the Siamese architecture imposes.