We presented a series of experiments that document our failure to apply state-of-the-art active learning techniques to predicting small molecule pKa. Our experiments evaluated regressing to a singular number and using the an ensemble of models to get a predicted variance, regressing to a Gaussian distribution and using the predicted variance, regressing to a NIG distribution and using the epistemic variance, and all the previous regression approaches but selecting the next molecule by the max absolute difference of its predicted label and the prior model's predicted label. We also evaluated restricting our model's clustering, adding additional clusters, changing our input representation, and a greedy approach that is fit to the test set or a validation set. In all of our experiments every active learning technique was not clearly better than adding extra training data at random.