To validate the new computaVonal methods presented in this workflow (applicaVon of HSP to POCs and the size-exclusivity predicVon algorithm), we setup this study as a screening approach. Not only does this minimise the search space that we are exploring experimentally, but it helps to ensure posiVve 'hits' in the final experimental validaVon. While screening approaches for chemical design are useful, the close integraVon of computaVon and experimentaVon in chemical design and discovery are advantageous and may be achieved via closed-loop discovery. Here, machine learning algorithms are used to suggest the next set of experiments to perform to most efficiently opVmise the objecVve; these methods have been shown to perform exceedingly well in chemical design and opVmisaVon studies. Thus, we envision the true uVlity of the predicVve methods presented in this work to be their applicaVon in closed-loop discovery workflows where they may be used to assess parVcular areas of chemical space without the need to perform resource-intensive experiments or simulaVons.