Material discovery has traditionally been carried out via a scientifically informed trial and error. Such an approach relies on the formulation of successive hypotheses oriented towards the restriction of the exploration space on the basis of the previous observations. Such a strategy relies heavily on experimentation and on domain knowledge, and while this can be beneficial for local optimization, it can also result in biased research. Nonetheless, materials science has relied on this approach for centuries. A new strategy in material discovery was introduced in the last decade with the use of descriptors-driven, high throughput screening. Any descriptive parameter (or set of) can fall into this category, but one might divide them into anthropic (i.e. human-readable) and data-driven descriptors, the latter being the most recent to be introduced.