Ye et al. introduced a combinatorial quantitative structure-activity relationship (QSAR) and machine learning approach to predict the emission wavelength, the experimental measurable qualitatively equivalent to E HL , of 11,460 organic fluorescent molecules against the corresponding experimental measurements. The authors reported the training results of R 2 = 0.663 (MAE = 0.2449 eV) and R 2 = 0.923 (MAE = 0.1253 eV) using the linear and nonlinear (random forest) models, respectively. The emission wavelength prediction was further refined by the inclusion of solvent effect with using 3000 distinct experimentally-recorded compounds. Based upon these aforementioned studies, one can see that the nonlinear models always provide more accurate electronic property predictions than the linear models. However, the corresponding interpretation generated by these property predictions still cannot be straightforwardly presented in terms of the intuitive chemical terminology, and that is due to the complex formulation in these models. Having the pros and cons addressed in the literature, we aim to demonstrate a progressive comparison in terms of model complexity, accuracy of the predictions, and the results interpretability. Such a comparison could provide an insightful perspective to benefit the field of virtual molecular design.