The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is traditionally successful at classification, there have been recent advances in using AL for regression tasks. Recently, regressing to a normal inverse gamma distribution has been shown to be effective at predicting molecular properties in the QM9 dataset. However, we present a series of experiments demonstrating that various state of the art AL regression techniques are indistinguishable from random selection for small molecule pKa prediction. Source code for this paper is available at https://github.com/francoep/pKa_activelearning.