We remark on a distinction between active learning [ ] and Bayesian optimization. Both sequentially collect training examples for a supervised machine learning model. In active learning, the examples are efficiently collected with the goal of reducing the uncertainty in the machine learning model. In Bayesian optimization, the examples are efficiently collected with the goal of, instead, finding the optimal material. BO is more efficient for finding the optimal material than active learning because it avoids collecting examples in regions of feature space that contain poor-performing materials, whereas active learning will do so to reduce the uncertainty of the model in those regions.