We applied the scoring system to the calculation of 38 TD-DFT functionals with respect of an ADC(2) calculation for five singlet states for 21,238 molecules, resulting in 828,282 single point calculations. The results of the calculations are open and available for the community and released in a large database that can be download for future research and projects, together with the data necessary to try different combination of scoring system and analysis. We used the scores we obtained to trained a graph attention network. Based on this, we have developed a machine learning tool that can quickly screen the quality of density functionals for a single or set of molecules, and recommend a subset of functional to be benchmarked on a specific molecule or chosen and used for a large set of molecules. This model, that we called DELFI, can be used to select a set of functionals to be tested on unseen molecules, as we demonstrated the applicability and transferability of DELFI on two in-silico experiments, namely choosing a common functional for the calculation of UV/Vis spectrum of a set of molecules with different degrees of functionalization and identifying a single functional to screen photophysical descriptors of a very large dataset of organic molecules.