Generally, the performance of MFBO for materials discovery is predicated on the surrogate model and the acquisition function. The surrogate model must (i) be fed features of the materials that are informative about the property (this relies on domain knowledge) and (ii) give wellcalibrated/honest uncertainty estimates while having a small number of training data points. The acquisition function for experimental planning must balance exploration, exploitation, and cost. Another factor for MFBO performance is the accuracy of the measurements of the material property; a challenge in the bona fide laboratory is that measurement noise is greater than in a computer simulation .