In Experiment 3 we show that performing a Greedy selection with respect to a singular cluster fails to generalize to the other clusters in the dataset. This is unsurprising, as by construction the Greedy selection is overfit to the dataset that is selected for. Thus, it remains to be shown if fitting to a validation set within a cluster would achieve good results on the test set of said cluster. In order to answer this question we created the "Largest Cluster Validation" set ( . As before we show that an optimal ordering of molecules is identified with the Greedy selection algorithm with respect to performance on the Validation set. However, this ordering of additional molecules has similar performance to the active learning methods and selecting molecules at random for the remaining unseen within cluster molecules in the "External/Test" set. Thus, the Greedy selection algorithm is only useful to provide an upper bound on active learning's performance relative to a particular set of data and does not transfer to other datasets even if they are from the same data cluster.