shows that there are molecules whose addition hurt final model performance. Additionally, models trained only on this cluster of data do not generalize to the out of cluster molecules, as shown by the poor performance on the "Rest" test set, which the models never see during training. Lastly, we point out that while it appears that the active learning methods may be successful in the Pearson's R evaluation on the within cluster test set (orange versus blue lines in Figure ), this effect is an artifact of randomness and disappears with more random seeds as shown in Figure , and that the supposed success is not reflected in the RMSE evaluation. We only ran 1 different seed on 1 fold of the data. The same models that were utilized in Figure are used here. Notably, there does exist an optimal ordering of molecules for the Test Set. We also report the results of the greedily selected model on the out-of-cluster molecules.