The second experiment considers the case when is not possible to run a proper benchmark on a set of molecules, either because the limiting size or the large number of compounds. The latter is, for example, the case in the field of material discovery, where a functional should be chosen to calculate the optical properties of a big dataset of molecules. A unique functional should be used, otherwise the relative properties would be meaningless if derived from different levels of theory, and at the same time, a proper benchmark for thousands of molecules is unrealistic. We take as example an organic photovoltaic (OPV) dataset that contains more than 50.000 entries, and we wanted to select a functional to screen the optical properties of these molecules to be used, for example, as molecular descriptor in a generative machine learning model. Usually, what would drive the functional choice would be chemical intuition and a literature survey, trying to find a functional that is supposed to perform well on that family of molecules.