Known constraints are those known to the researcher a priori to commencing an experimental campaign. They can be interdependent, non-linear, and result in noncompact optimization domains. Our group's previous work focused on extensions to BO strategies such that they would avoid sampling constrained regions. Another flavor of known constraint problems may arise when the optimization domain contains degenerate solutions (e.g. with respect to scaling, composition or permutation of input parameters). Baird et al. recently described the relationship between search space reducibility and BO performance for an application in solid rocket fuel packing. Contrastively, unknown constraints are those constraints on the optimization domain which are a priori unknown, and, much like the objective function, must be resolved on-the-fly by sequential measurement. As the types of optimization constraints encountered are diverse, in this work we chose to focus specifically on nonquantifiable, unrelaxable, simulation, hidden (9:NUSH) constraints, according to the taxonomy by Digabel and Wild. • Non-quantifiable: for a nonquantifiable constraint, the researcher is returned only binary information about whether the constraint has been satisfied or violated. This is contrasted with quantifiable constraints, where some auxiliary information is available about the proximity of the experiment to the feasible/infeasible boundary.