The two key components of BO are a surrogate model and an acquisition function. The surrogate model, with "surrogate" hinting at "substitute for the experiment"-is a probabilistic model for the structure-property relationship. It is trained on all observations from past experiments. The surrogate model cheaply predicts the properties of the unevaluated NPMs and, critically, quantifies the uncertainty in its predictions. The acquisition function is used to make the decision of which NPM to select for the next experiment. It uses the surrogate model to score the utility of selecting each unevaluated NPM for the next experiment by striking a balance in the exploitation-exploration trade-off. The acquisition function is maximal in regions of the NPM design space where (i) we be-lieve high-performing materials will reside and/or (ii) we are uncertain about the structure-property relationship. The acquisition function relies on the surrogate model to give a resource-efficient, intelligent, active search for optimal NPMs in a wide variety of contexts.