Work still needs to be done on both absolute and relative binding affinity predictors to ensure that they are learning robust models of the intermolecular interactions. Future work should focus on including additional symmetries involved in RBFE in the predictive models, such as cycle closure. Including the L consistency term, focused on the symmetry of the relative and absolute binding affinity prediction, was able to increase the performance of our RBFE predictions, therefore including higher order symmetries of the RBFE problem may enhance the performance of future models. Rotational symmetries of the inputs can be addressed with SE(3)-equivariant convolutions, 36 rather than a rotational loss. Adding uncertainty quantification to the predictive model could enable large performance improvements with fewer ligands during finetuning by focusing on the ligands with the greatest uncertainty according to the RBFE model.