Metal-organic frameworks (MOFs) have revolutionised the field of materials science, offering a wide range of applications from photocatalysis 1 and semiconductors to energy storage and gas separations. The unique appeal of these "designer" materials lies in their extraordinary tunability, a feature attributed to their modular construction of inorganic nodes and organic linkers in a topological net. As such, the number of experimentally synthesised MOFs currently spans over 100,000. However, the expansive chemical space of organic linkers hints at an almost limitless possibility for MOF combinations, and therefore functionalities. This capacity for customisation is reflected by the vast number of hypothetical MOF structures that have been proposed to-date. Navigating this design space is a task of no easy undertaking. It is of course not possible to synthesise and test every MOF in a laboratory, given the sheer number of already existing and hypothetical structures. Therefore, high-throughput computational screening (HTCS), aided at times by the use of machine learning, is often used to seek promising MOF candidates for a particular application. This approach, which begins with a collection of structures and filters them for a desired key performance indicator (KPI), relies on the selection of top performers from existing databases of materials. One of the main limitations of conventional HTCS is that the distribution of known materials are usually skewed towards functionalities far away from the intended application. A good example would be the Department of Energy (DOE) density targets set for vehicular hydrogen storage systems, which are currently defined on a gravimetric and volumetric basis to be 6.5 wt% and 50 g L -1 , respectively. While there are many known MOFs that achieve the gravimetric target of 6.5 wt%, few are known to achieve the volumetric target of 50 g L -1 and, in fact, many fall within the deliverable range of 0 -30 g L -1 . It is therefore crucial here (and in many other applications) to offer a significantly expanded selection of potential structures for HTCS efforts. This allows researchers to refine their search for the best materials to synthesise, considering a range of aspects (such as linker availability, ease of synthesis, material costs, and possible stability challenges) that go beyond what traditional HTCS studies typically address.