The number of molecules that obey Lipinski's rule-of-five for oral bioavailablity, otherwise referred to as "drug-like" chemical space has been estimated to be 10 60 . Even with the is a state of the art method available in classification tasks. Soleimany et al. achieved state of the art performance and showcased the ability of their method to succeed in active learning tasks on the QM9 dataset for molecular property prediction. However, the QM9 dataset is much more constrained than a typical drug discovery context, both in terms of molecule size and which atoms are available. Thus, we sought to adapt their approach and benchmark it against other active learning techniques for predicting small molecule pKa for a drug discovery context. We analyzed active learning selection by the variance of the predictions of an ensemble of models, the predicted variance of a single model by regressing to a Gaussian, and the epistemic uncertainty of a single model by regressing to a NIG distribution of feedforward neural networks trained on clustered cross-validation (CCV) splits of the OPERA pKa dataset. We show that while a greedy selection ordering shows that there is an optimal selection of molecules to achieve maximal performance on a small subset of held out compounds, none of the active learning approaches achieved better performance than selecting from the withheld molecules at random. Source code for our models, training procedure, and scripts for generating the data in this paper is available at .