Given the failures of Experiments 1 through 7 at generalization, and that larger models tend to generalize better, we elected to take our earlier Morgan fingerprint based method and dramatically increase the number of layers in the model. The maximum number of hidden layers in our hyperparameter sweep was 4 (Figure ), so we evaluated the same general model architecture as our deployed model but used 9 hidden layers instead. For this experiment we trained one seed on the "Largest Cluster 02" data split, and since this model is We show the R and RMSE results on the within cluster test set of the "Largest Cluster 02" for a 4 epoch trained model with regresses to a NIG distribution. The additional seeds allow us to conclude that there is no difference between active learning selection or selecting molecules at random. larger we also trained for 4, 40, or 400 epochs for each active learning selection method. The results of this experiment are shown in Figure . We find that there is minimal difference between picking molecules at random and using an active learning approach and that the much larger model's final performance on the withheld clusters is similar to the results we found in Experiment 5 (Table2). The smaller model used in Experiment 5 only had 2 hidden layers, as compared to the model used here having 9. Thus, more than doubling the number of hidden layers in the model was insufficient to rescue the poor results we have observed.