Progress in the areas of chemistry and materials science is often contingent on the targeted design of new molecules, materials and manufacturing processes that supersede their predecessors with respect to some measurable property. As a fundamental aspect in numerical science, it is not surprising that many design/discovery challenges in these areas can be cast as optimization problems. Solutions to such optimization problems are determined by systematic variation of input parameters from an allowed set with the goal of maximizing or minimizing a property or set of properties. celerate scientific research, their practical application is limited by the lack of flexible software and algorithms tailored to the specific requirements of experimental science. One pervasive example is the inability of many BO algorithms to handle parameter space constraints. Although types of constrained optimization problems are diverse, we focus the discussion in this work on two types.