In this work, we are exclusively concerned with cases where c(x) is a priori unknown, incurs a non-negligible cost for its determination (i.e. is still expensive to evaluate), cannot be relaxed, cannot be decoupled from evaluation of f (•), and returns binary information about the constraint. Let constraint function evaluations ỹ = c(x) return 0 if x ∈ C and 1 if x / ∈ C, i.e. ỹ ∈ {0, 1}. During such a constrained optimization campaign, the experiment planner collects both datasets of continuous-valued objective function measurements