In this article, we explain, demonstrate, and advocate Bayesian optimization (BO) to actively and efficiently search for NPMs with an optimal property for a given adsorption task. Active, because each BO iteration performs three consecutive steps: (a) conducting an experiment on an NPM, (b) updating our belief about the structure-property relationship, and (c) selecting the NPM for the next experiment. See Fig . for a high-level illustration. Efficient, because BO makes a data-informed decision on the NPM to select for the next experiment, while balancing: (a) exploitation of our current (uncertain!) data-driven belief about the structure-property relationship to pick an NPM that might have the optimal property and (b) exploration of regions of the NPM design space where our belief about the structure-property relationship is weak to pick an NPM we are uncertain about.