This study aims to systematically compare multiple data selection strategies, AL with different acquisition functions and IPs selection, for identifying informative MOFs for methane adsorption prediction. Each method is applied independently to the same MOF dataset, resulting in subsets of MOFs deemed most informative for training GP models. The structural diversity of these subsets is analyzed through principal component (PC) projections and property histograms, highlighting differences in selection patterns across methods.