Given the range of hypothesized experimental limits, ranging from 0.5 < RMSE < 1.0, we sought to establish if our models are as performing as accurately as possible given this dataset. To do so, we downsample the training dataset to some smaller size, train three models and ensemble their performance on the test sets. Repeating this a different sizes of downsampled training sets generates a performance trajectory as a function of training set size (Figure ). We observe that the performance trajectories for both the fastprop and chemprop models are similar, despite representing fundamentally different modeling approaches. We also observe that the model performance on the SolProp test set plateaus after only 500 experiments (∼5000 data points) are included in training for both the chemprop and fastprop models. Similarly, the performance on the Leeds test set plateaus for the chemprop model after only 2000 experiments (∼20,000 data points), although performance of the simpler fastprop model takes slightly longer to plateau.