There are two major choices that must be made in the optimization procedure, namely the prior over the functions, and the acquisition function. The prior expresses assumptions or gives information about the function being optimized, while the acquisition function is used to determine the next most favorable point for evaluation, most likely to reduce uncertainty in the function's possible values. In this section, we briefly review the general Bayesian optimization algorithm, before discussing our application to optimize conformer geometry. For an overview of the Bayesian optimization formalism and a review of previous work, see and Shahriari et al. .