After an initial SNOBFIT call with a randomized space-filling design, exploitation is realized by fitting local quadratic models of the objective function for each point that has been evaluated. Then, SNOBFIT suggests new points by optimising these quadratic models. In addition to exploitation, points in unexplored regions of the problem domain are suggested. We treat constraints as hidden constraints. That is, if a point suggested by SNOBFIT violates at least one constraint, we return NaN as objective function value.