The first requirement to perform computational materials discovery is to have access to open databases with cleaned and computation-ready structures, as well as associated parameters. Given the size of the databases (typically ten of thousands of structures), screening studies are computationally expensive, so two approaches are favored. In the first approach, efforts are made to reduce the number of structures in the database while increasing the diversity of structures (from a structural perspective, or based on the underlying chemistry or reagents used in synthesis). An alternative method is to perform multi-stage screening, with a first filter with low computational cost to calculate properties that encode the most pertinent information for the application chosen. Screening studies often rely on assumptions in material models to accommodate a wide range of diverse structures, which tends to decrease precision in the measured properties. Consequently, research groups advocate for providing datasets with high levels of precision, such as DDEC charge sets for adsorption calculations with GCMC. Despite their promising opportunity to accelerate the process of discovering new materials based on structural features, the data-driven approaches still suffer from a number of shortcomings, including the availability of structural data, its accuracy and synthesizability of the hypothetical structures. Screening studies often currently neglect the synthesis data and promote materials that might not be relevant for designing real materials. With the advent of text-based information processing methods, it is however becoming possible to automatically review a large corpus of scientific articles to extract synthetic properties from the published literature (see Figure ). In the near future, the aim of these directed approaches would be to propose synthetic routes to experimental chemists, given a limited number of constraints such as a set of reagents, the material available and the desired properties.