Having defined a numerical evaluation of each functional and collected a large amount of data on QM8 dataset, we trained a neural network to predict the score for each functional given a certain molecule. The goal of such a model is to quickly screen density functionals and cheaply provide a recommendation about a set of functionals within a certain threshold of score that should be properly benchmarked, potentially with a bigger basis set and against experimental values, to find the most accurate functional to compute vertical excitations and UV/Vis spectrum of a molecule. We represented the molecules of the QM8 dataset using a 2D graph and trained a GAT for multitask regression, where a final message passing layer gives a set of 38 scores (one for each functional) for a given input molecule (Fig ). Details on the implementation are describe in section 7. The test set used to analyze the performances of DELFI is composed of 2101 molecules (10% of the size of the dataset). We obtained a R-square of 0.7 and a minimum average error (MAE) of 6.5, for scores in the range 0-100, on all the 79838 scores predicted on the test set. We previously highlighted how a functional with a score above 60 might be worth it to be properly benchmarked, so when we consider the predicted scores that are higher than this threshold, we have a true positive and negative accuracies of 76% and 95% respectively. On average, 6 functionals per molecule are predicted above this threshold and, in 84% of the cases, the top performing functional is rightfully predicted in these six and 34% of the time is correctly predicted with the highest score. When we considered the matching between which are the top five functional calculated and predicted, in 10% of the time 5/5 functionals matches, reaching 78% considering when at least one match is found. Before applying DELFI on test cases, it is important to underline the domain of applicability and when it should be used, remembering that the top functional does not necessarily reflect the one with the closest energies to experimental values, but only predicts the functional that matches the most the reference method at the level of theory used for the dataset, which carries its own limitation and experimental error . If possible, the top recommendation should be benchmarked. The score of the functionals are relative to an higher accuracy calculation, which still carries its own limitations. The dataset used to train the model contains relatively small organic molecules, and does not include, for example, transition metal complexes. The scores are obtained considering only five singlet states at a single geometry in gas phase. As such, the indication provided by DELFI is meaningful for the calculation of a manifold of singlet vertical excitations, and might yield inaccurate recommendations when including solvent effects, considering triplet states or excited states dynamics. Only 38 functionals are tested, but ranging among different types and different contributions of hybrid parameters and long range corrections. The best functional might not be in this list, but the recommendation of DELFI could be useful to identify the family of functionals that perform the best.