From the perspective of material chemists, the holy grail is to design the most promising materials for a given application . Currently, the most commonly used strategy is to find the best materials through brute force screening of these materials library. However, as we are interested in a small subset of top-performing materials, most of the effort in these brute-force methods is spent on computing properties of those materials that are not interesting. As the number of structures in these databases has grown significantly, several groups have started taking a different approach in searching more efficiently within this infinite chemical space of MOFs, using methods such as diversity-driven searches , and active learning based searches .