unknown constraints which can only be evaluated alongside the objective functions and known constraints which can be evaluated before any objective evaluation. Known constraints are usually defined by domain expertise to pre-emptively minimize sample wastage . Unknown constraints present a more challenging optimization scenario since the goal of efficiently achieving multiple objectives needs to be balanced with enough sampling of the feasibility boundaries to learn the constraints. This was previously demonstrated by Khatamsaz et al. for alloy design , as well as by Cao et al. for liquid formulations , where in all cases an appropriate machine learning model needs to be trained a posteriori to classify whether samples are feasible or not. Based on the good performance of EGBO on both the self-driving AgNP platform and the synthetic problems, we now explore how to adapt the algorithmic framework to reduce sampling wastage.