In this work, we seek to identify a subset of MOFs that are shared across multiple methodological paradigms, thereby strengthening our confidence in their importance for subsequent screening or validation. The IP approach ensures an even representation of the underlying covariance structure while each AL/BO method pinpoints MOFs that maximize potential improvement or reduce predictive uncertainty. By independently selecting MOFs through these different criteria, we obtain multiple, potentially divergent sets of MOFs. However, by intersecting all these sets together, we isolate a consensus group of MOFs.