We explored the effect of combining datasets together and performed 5-fold cross-validation to draw more rigorous conclusions. After observing the exceptionally poor performance of all models on the CDEx dataset, we excluded it from the combined dataset. The GBRT method was excluded from this analysis to compare the Chemprop and ChempropMultiFidelity models using the D-MPNN solvent representation (which cannot be integrated into the GBRT method). ChempropMultiFidelity achieved a mean MAE, RMSE, and R 2 of 27.78 nm, 47.13 nm, and 0.80, respectively. These scores outperformed Chemprop in all metrics, but there was overlap in the standard errors of all three scores. The complete results are shown in Table . The prediction errors on this larger, combined dataset are larger than While the accuracy of a model is very important, the ability to quantify uncertainty in predictions can greatly increase model utility. The level of confidence in a prediction or set of predictions can be used to motivate the selection of candidates that are most likely to succeed in experimental validation or to inform the choice of new measurements that will improve the model through active learning techniques. There are a plethora of methods available for estimating the uncertainty in NN models, and reviews of these methods have not found one method that consistently performs others across datasets and evaluation metrics. However, one approach that is often used because of its ease of implementation is the ensemble variance as a measure of epistemic uncertainty. We evaluated the effectiveness of this method for quantifying the uncertainty in our Chemprop model. The parity plot in Figure shows that the variance of an ensemble of five models is indeed high for many of the predictions that fall far away from the parity line. However, closer examination by plotting the square root of the ensemble variance versus the absolute prediction error gives a much more sobering view of the effectiveness of this uncertainty metric. In fact, the Spearman rank correlation for this set of predictions and uncertainties is only 0.52, suggesting that one should not necessarily consider the rank order of the prediction uncertainties to be a good approximation of the rank ordering of the prediction errors.