In summary, we have developed fast, reliable models to predict DFT-level descriptors for carboxylic acids, primary alkyl amines, and secondary alkyl amines. We obtained highaccuracy predictions not only for descriptors containing aggregate information for the conformational ensemble (Boltzmann-averaged properties) but also for descriptors that encompass the extreme flexibility of these compounds (minimum and maximum property values). These models relied on the construction of large libraries of DFT-derived descriptors that are useful in and of themselves. To accomplish this, we have developed a user-friendly, automated script for collecting moiety-specific descriptors that have been key to mechanistically insightful modeling efforts. This case study further demonstrates the efficacy of applying 2D and 3D GNNs to predict DFT-level steric, electronic, and stereoelectronic descriptors of conformer ensembles from accessible molecular representations, such as the 2D molecular graphs or conformers derived from inexpensive molecular mechanics. While 2D models performed well for global molecule-level descriptors of the conformer ensembles, poorer performance was noted for local atom-and bond-level descriptors particularly sensitive to 3D geometry, directionality, or intramolecular non-covalent interactions. These difficult-to-predict descriptors include dipole moments, hydrogen NMR and NBO (for acids and amines), Sterimol values, and nitrogen pyramidalization and lone pair properties (for amines). To overcome these challenges, we developed 3D models that encode the geometries of readily accessible MMFF94-level conformers, striking a balance between geometric expressivity and the cost of conformer generation. The 3D models improved predictive accuracy for most descriptors, with notable gains in performance for hydrogen atom NMR and NBO descriptors, Sterimol values, and amine pyramidalization. Given the greater cost of the 3D models and conformer generation, we recommend the use of 2D GNNs for most molecule-level descriptors, atom-level descriptors without acute sensitivity to geometric characteristics, and other descriptors for which the 3D models worsen predictive accuracy, such as IR frequencies of acids. In contrast, using 3D GNNs can substantially enhance prediction performance for local atom-and bond-level descriptors sensitive to molecular geometries and/or noncovalent interactions, even when encoding computationally inexpensive conformers. In the future, additional modeling could focus on probing the limitations of predicting DFT-level descriptors from cheaper 3D representations, enhancing the ability of 2D and 3D models to capture intramolecular noncovalent through-space interactions, and generally improving the ability of GNNs to model the dynamic range of properties that result from conformer ensembles.