Thanks to their high surface areas, crystallinity, and tunable properties, metal-organic frameworks (MOFs) have attracted intense interest as next-generation materials for gas capture and storage. An often-cited benefit of MOFs is their large number of structures and compositions. However, this design flexibility also has drawbacks, as pinpointing optimal compounds is time consuming and costly using conventional experimental approaches. As a consequence, computational approaches are garnering increasing importance as a means to accelerate the discovery of high-performing MOFs. Here we demonstrate high-throughput techniques for predicting the performance of MOFs for CO2 capture and the storage of gaseous fuels such as methane and hydrogen. Empirical screening strategies are compared with those employing direct atomistic calculations. The performance of the most promising compounds are synthesized and tested experimentally.