Computer and machine learning scientists have studied BO with unknown constraints stretching back more than a decade. Gramacy and Lee were among the first to do so, employing Bayesian learning strategies to estimate both the objective and constraint function. The integrated expected conditional improvement (IECI) acquisition function was introduced which enabled incorporation of partial information from constraint violations into the optimization procedure, a variant constrained problem which we do not address in this work. Gelbart et al. introduced the feasibility-weighted acquisition function (FWA approach described in Sec. III.B) but for scenarios where the f (x) and c(x) can be decoupled and evaluated independently. Snoek et al. describe a similar approach but for scenarios where the two functions are innately coupled, where constraint violations come from e.g. a crashed objective simulation. More recently, Candelieri et al. presented SVM-CBO, which uses a consecutive two-stage approach to optimization with unknown constraints: i) a support vector machine (SVM) is used to estimate the constraint boundary using measurements of c(x) exclusively, and ii) BO is used to optimize f (x) using the SVM estimate of c(x) as a known constraint. This method lays the foundation for our FCA approach (described in Sec. III.B), with a few key differences: i) we use a variational GP classifer to estimate c(x) as opposed to an SVM, and ii) our evaluations of c(x) and f (x) are not separated into two distinct stages. Instead, a fresh known constraint function is inferred by our classifier at each iteration in light of the most recent c(x) evaluations.