DELFI can quickly predict the scores for each of the molecules and analyzing them can drastically facilitate the decision. We ran DELFI on the dataset and we obtained in few seconds a clear indication of the functional to be used, indeed ωB97M-V turned out to be the top performer on 38747 out of 51281 (75%) of the molecules, with the second one being M11 in only 5% of the time (2578 molecules), giving a statistically robust indication of the functional to use. Interestingly, this functional was only around 2% of the time the top performer on the training set of the model, showing how the graph representation allowed transferability of the prediction and not simply replicating the distribution of the scores of the learning. However, it should be noted that for ωB97M-V, even if with the highest value, a value higher than 50 is predicted in only 69% of the molecules, with the percentage decreasing to 32 for scores higher than 60, alerting on how generally TD-DFT might perform poorly on these molecule and suggesting further analysis on single picks to have a more confident understanding of the optical properties of this class of compounds.