For both random and K-Means splitting, we trained an ensemble model for each split with five different weight initializations and averaged the predictions for all molecules across the ensemble. We report the average performance metric across the five splits, using the standard deviation to estimate the uncertainty in the reported metric. To compare the performance of models trained with the RIGR and native featurizers, we performed a two-tailed paired t-test with a significance level α of 0.05 to assess any significant differences. For comparisons involving more than two models, pairwise t-tests are conducted with a Bonferroni correction to control for familywise error rate (FWER). More information about model comparisons using statistical tests is given in Section S4.1 of the SI.