Multi-fidelity Bayesian optimization (MFBO) 9, takes advantage of multiple types of experiments that trade-off fidelity and affordability to search for a material with an optimal property while incurring the minimal cost. MFBO modifies the experiment-analysisplan loop of standard BO in Fig. by extending: (i) the surrogate model, to (a) predict the property of materials according to experiments of all fidelities and (b) capture the correlations between the material properties according to each experimental fidelity, enabling observed outcomes of low-fidelity experiments to inform predicted outcomes of high-fidelity experiments, and (ii) the acquisition function, to pick the next material and the next experimental fidelity, while balancing exploration, exploitation, and the cost of the different experiments. In turn, MFBO leverages low-fidelity experiments to cheaply scope out which regions of materials space contain (i) poor-performing materials, to avoid wasting resources on high-fidelity experiments there, and (ii) high-performing materials, to focus high-fidelity experiments there. MFBO (or its parent, multi-information-source BO ) has been scarcely applied to materials discovery.