All of our active learning selection methods are attempting to utilize a model's predicted uncertainty about the pool of unlabeled molecules in order to select the molecules that the model is most uncertain about to add to the training data. This approach is reasonable, but has failed to outperform random selection in all of our experiments. Thus, we experimented molecules based on the maximal absolute difference between the previous predicted label and the new predicted label. We hypothesize that the molecules whose predicted label changes the most between active learning cycles would thus be the most informative molecules to add to the training set. We implemented this selection criteria and evaluated each of the 3 classes of models we have while training them for 4, 40, or 400 epochs on the "Largest Cluster 02" data split (Figure ). Given that there is only one seed being shown here, none of the results are promising enough to suggest a difference between this active learning selection approach and picking molecules at random for every type of model regression. We also find that this new selection criteria fails to generalize to out of cluster molecules again. to outperform randomly adding molecules into the training set. Thus, the question remains if our approach failed, or if it was a dataset issue. In order to address this, we utilized the CZO dataset, which is a differently prepared set with 2 accompanying independent test sets. Upon this new dataset we performed 2 experiments. First, we recreated our initial experiment by utilizing our model as is on the CZO dataset (Figure .