Overall, our GIN model produced via transfer learning showed a major increase in predictivity compared to the QSRR model, when applied on our test set (Figure ). This finding is evident by the significant metrics the GIN produces when compared to the QSRR model, with a notably higher R 2 in addition to the MAE and RMSE being less than half of those for QSRR as shown in Figure . While the GIN R 2 of 0.82 for the transfer learning test set is lower than the 0.91 produced during initial model training, it is an encouraging result given the low number of training analytes, and the irregular distribution of retention times. Notably, QSRR models analytes with lower retention times better than those with greater retention times, as seen in Figure , where as the GIN does not have a range of retention times where performance is dramatically different. Additionally, key quantitative structural activity/retention relationship descriptors, Wildman-Crippen LogP and topological polar surface area, in Table , do not linearly correlate with measured retention times of test compounds. These results indicate that the GIN model is capable of capturing non-linear relationships between analyte structures and retention times when using a small dataset for transfer learning. Overall, these results show that retention time modeling with GINs and transfer learning can provide a significant advantage over traditional QSRR approaches when presented with low amounts of data.