We want to identify BO strategies which efficiently optimize the objective function f , while preferably also limiting the number of infeasible measurements L. To do so, a compound acquisition function is used, which we term the feasibility-aware acquisition function, α c (x; M f , M c ), which depends on the predictions of two surrogate models: i) a regression surrogate, M f , fit to the objective function measurements in D K f , and ii) a classification surrogate, M c , which is fit to the constraint function measurements in D K c . α c combines these predictions in one of several ways to balance adherence to sampling in parameter space regions believed to have promising objective values with avoidance of regions believed to be infeasible. Feasibility-aware acquisition functions are described in Sec. III.B. Algorithm 1 shows pseudocode for BO subject to unknown constraints. Note that, while a scalar-valued objective is used for illustrative purposes here, one may easily extend this framework to the multiobjective setting by using, for example, an achievement scalarizing function.