For the training and validation set data displayed in Figure , the stratified splitting employed successfully generated the validation set such that it mirrors the overall distribution of the larger training set. With the stratified splits generating training and validation sets with comprehensive coverage of the measured retention times, we were confident that a fitted model would be generalized to perform across the entire range of chemical space associated with the measured retention times. In the context of transfer learning, this would ensure that the initial model would effectively learn how to operate on the graph representations of molecules and produce latent variables for successful retention time prediction. Conversely, the test set for the goal of the transfer learning task does not exactly match the distribution of the training compounds and their RTs displayed in Figure . Furthermore, the sparsity of the training set for transfer learning, due to its membership of only 80 cyclic peptides, could potentially complicate the effective generation of a predictive model for relating structure to retention time. For the transfer learning, the test set was treated blind with no retention times provided. These qualities regarding the training and testing sets for the transfer learning present a challenging and rigorous scenario to evaluate our modeling approach.