DELFI gives system-specific recommendation, not learning scores pattern in the test set, but discriminating chemical differences in the evaluation of the functionals. Although the scoring system we developed is generalizable to any level of theory and number and multiplicity of states, a current limitation of DELFI lies in the generation of the training data, which are obtained using as a reference ADC(2), including only a manifold of singlets states, the exclusion of solvent effects and diffuse basis function, the use of only 38 functionals and the limited region of chemical space considered. We hope we made it clear throughout the text the range of application of DELFI and the goal of it: quickly screening density functionals for TD-DFT and produce a recommendation on which ones should be properly benchmarked. However, it is worth it to notice how the results of the scoring system applied to QM8, and consequently the recommendations of DELFI, are in line with extensive literature benchmarks. As all the data generated is freely available, as well as the code for the scoring system and the parameters of the model, we plan and encourage future effort to improve generability and applicability of the model, improving the quality of the training data and adding new functionals. Nonetheless, we strongly believe in the potential of DELFI to help the community for the task it was developed, so we wanted to provide an intuitive and user-friendly way to run and analyze DELFI. For this goal, we released a web application, freely available, to obtain prediction on unseen molecules, and analyze already and new calculated/predicted results. Overall, we believe that all the work developed will drastically facilitate to solve one of the most common hamletic doubts in computational chemistry.