Density functional theory (DFT) , and its time-dependent formulation (TD-DFT) for calculations beyond the electronic ground state, are the most widely used computational methods for the calculation of electronic energies, due to their hardly beatable accuracy/cost trade-off for medium-to-large size molecular systems. However, the main limitation of DFT lies in the impossibility of knowing the exact form of the electron exchange and correlation for molecules and the need to use an approximate functional . Although a plethora of exchange-correlation functionals have been developed , even reaching high accuracy for specific problems and universal recognition in the community , there is arguably a unique solution for the combination system/property/functional . This is particularly true for the calculation of electronically excited states, where several additional challenges rise due to the treating of highly correlated and multi-reference objects as excited states . TD-DFT, in its linear response formulation , the most popular one, and in the limit of the adiabatic approximation cannot describe double/multiple excitations and intersection seams between ground and first excited state potential energy surfaces , and it problematically describes highly delocalized states as charge transfer or Rydberg states . While the first two problems cannot be overcome within the adiabatic approximation, for the latter several modifications have been introduced to mitigate the errors of ground state functionals and improve the performances of TD-DFT, leading to the development of long-range corrected functionals . However, a general recipe for a functional that can satisfactorily and universally describe optical molecular properties has not been found, and arguably cannot be found. As a consequence, since DFT and TD-DFT popularity is so high, one of the most common questions a computational chemist faces while approaching a problem to solve is: which exchange-correlation functional should I use? A generally accepted approach to find an answer is to benchmark a bunch of functionals against the results obtained with a higher-level method or experimental results , if available. In this case, a one-by-one state comparison is needed, since it is missing a general quantitative way to estimate the quality of an excited states calculation. Additionally, this approach is prohibitive when studying large-size molecular systems, or when deciding a unique functional to screen optical properties of a large set of molecules, for example in the field of material discovery. In these cases, a single pick is made, based on literature research and chemical intuition. More generally, both approaches are limited to a small subset of choices and might easily leave out the outperforming functional. Alternatively, general qualitative indexes have been proposed, or focusing on specific types of excitations, but always requiring actually running the calculations a-priori . Machine learning could facilitate this task by learning patterns between molecular properties and functional performances, as promisingly shown very recently for ground state properties . However, in the case of optical properties like UV/Vis absorption, many electronic states need to be considered, and their energies are not the only quantities to consider when evaluating the performance of a functional, but also the character, intensity and order of the excited states need to be properly described by TD-DFT . Indeed, a wrong description of the order, character, and brightness of two states might lead to misinterpretation of photochemical pathways and reactivity. An easy yet important example can be the canonical nucleobases UV/Vis absorption, where a misinterpretation of order and character of L a , L b and nπ* states would lead to a wrong interpretation of their photophysical properties . To the best of our knowledge, there is not a systematic, universal, and transferable excited states evaluation scheme that is able to keep together all this information for a bunch of excited states that could be used for the assessment of the goodness of a method or functional. In this paper, we aim to fill this large gap by introducing a general scheme to evaluate excited states calculations obtained with a lower accuracy electronic structure method with respect to a more accurate one. In particular, we developed a scoring system that considers at the same time differences in energy, character, and brightness of a certain number of states and then assigns a final score between 0 and 100. By applying this scoring system to the calculation obtained with 38 different functionals of different quality on a dataset of organic molecules, we will be able to transform the functional recommendation in a regression problem, train a graph attention neural network (GAT) , and provide a transferable TD-DFT functional recommender. We called the recommender DELFI (Data-driven EvaLuation of Functionals by Inference) and we will show here its applicability to provide a set of functionals, and their relative score, to be tested and benchmarked as outperforming a given molecular system or database. Finally, we provide a web application that we developed to analyze the scoring system on which the data is trained and to directly run DELFI by just providing the SMILES of a molecule, including analysis tools of its results in an interactive and user-friendly way, providing a powerful tool for chemist of any level of programming experience approaching a TD-DFT calculation.