It is found that the TD-DFT values for all but one of the predicted 𝑆 1 excitation energies' and oscillator strengths' of these candidates lie within the expected, respective double standard deviation of model testing errors 2𝜎 err . The exception in this case is the 𝑆 1 oscillator strength of the candidate AQ-Red. The reason for this far-off prediction is likely due to the fact that there are relatively less available red absorbing candidate molecules inside the overall chemical space -which holds true for the training set as well. Thus, when predicting the properties of candidates in such underrepresented regions, the model is more likely to fail as there are only few reference molecules to draw meaningful similarities from. This scenario can thus be understood as an extrapolation attempt rather than interpolation -which remains a challenging task for practically all ML models.