We find high protein and ligand similarity between the BindingDB Congeneric Series set and the external datasets from Mobley et al. and Wang et al. . We provide the minimum protein distance, determined via a global alignment with no parameters and no gap penalties (Biopython.align.globalxx), and highest ligand similarity, determined with RDKit's FingerprintSimilarity function in Table . BACE has the minimum protein distance with a protein in the BindingDB dataset and both CDK2 and PTP1B have greater than 95% of their ligands in the BindingDB dataset. This is likely why the correlation and error on CDK2 are nearly perfect with no finetuning. We would expect similar results for PTP1B since it shares a nearly identical minimum protein distance and similar percentage of ligands found in the BindingDB set, but PTP1B has lower correlation and higher error than CDK2 in the no-shot evaluation. Our models do not show the same level of RBFE prediction correlation as Jiménez-Luna et al. , however, the RMSE of the predictions is about the same or less. Correlation is a poor predictor of relative binding affinity performance, due to the low range of affinities in a congeneric series. If, for instance, each ligand in a congeneric series has an identical affinity value, then there is no way to measure a predictive Pearson's R correlation. Therefore, it is difficult to determine if the model built by Jiménez-Luna et al. demonstrates more generalizability than our models. When we evaluate our Siamese network on the more difficult Schindler et al. dataset, we again see much variability in our models performance across the different targets. Our model is unable to match the performance of the FEP+ model in correlation of prediction (Figure , S5, and S6) without using the largest amount of finetuning we explored. Examining the RMSE of the RBFE predictions shows the Siamese network outperforming the FEP+ method on all of the targets, when all of the finetuning data is introduced. The lower correlation and lower error are due to our Siamese network predicting values around the mean of the data. FEP predictions are not dependent on the labels of the data, since there is no training necessary, and therefore would eliminate these sort of predictions.