An alternative, less data-intensive avenue would be to use active learning as a means to reliably explore the chemical space of MOFs. In either case, a viable pathway towards developing a general-purpose, highly accurate ML potential for MOF screening applications exists provided that the appropriate training data, ML architecture, and feature vector can be combined. If such a method were to emerge, it has the potential to shift paradigms not only in the screening of MOFs for carbon capture, but in materials modelling in general.