Across all methods examined in this work, 1, 12, and 13 were the most consistent with predictions within 1 logPtol/w unit with the exception of the MM method shown in Table , which yielded an error of 1.20 for 13. For the single conformation QM methods (QM, DLPNO-Solv-ccCA, PBE/cc-pV∞Z), the unsigned errors for 7, 11, 14 were less than 0.18, 0.25, and 0.60 logPtol/w units, respectively, while for the ensemble-based methods (MM, Mixed, MM-1, ML, S20), In the case of 7, the flexibility of both the propylene linker between the tertiary amine-phenothiazine moiety and the ethylene linker between tertiary amine-alcohol group induces the possibility of generating numerous conformers within 10 kcal/mol of the absolute minimum. Moreover, these conformers may have distinct polarity and steric profiles which can skew the relative solvation free energies by up to 10 kcal/mol which translates to a logPtol/w difference of 7.36 units. This explains the distributions observed in the logP histograms (Figure ) and heat maps (Figure ). However, for 5, all the ensemble-based methods yielded lower errors than all the QM methods by an average of 2.35±0.41 logPtol/w units, which was verified to be statistically different via the two-tailed unpaired t-test. For 6 and 9, while the average logP for the ensemble-based methods was lower than that for the single conformation QM methods by 0.74±1.06 and 0.78±0.47 logP units, respectively, the datasets were not statistically significant. These molecules have less rotational degrees of freedom, which would suggest that utilizing ensemble-based methods are advantageous for more unsaturated drug-like molecules. Therefore, a careful selection or weighting of conformations in the thermodynamic ensemble is essential for predicting solvated properties that depend on calculations in multiple environments, such as logPtol/w or pKa. In screening viable conformations, consider the 3D structures for 7, and 16, which both have N,N-disubstituted piperazine rings. One key feature of the piperazine functionality is that the net dipole is significantly affected by conformation of the ring. The structure shown on the left in Figure shows the pseudo-diequatorial moieties about the piperazine, while the structure on the right shows the pseudo-diaxial conformer. In the pseudo-diequatorial case, the nitrogen lone pairs are in a trans orientation, thus minimizing the net dipole. This conformer is favored in the less polar solvent, toluene. The pseudodiaxial conformer is favored in water as the nitrogen lone pairs are cis thus maximizing the net dipole. The nitrogen lone pairs are localized as HOMO-2 and HOMO-1, and so only one of the MOs (HOMO-2) are visualized in Figure . This feature for 7 and 16 was observed for pairs of rotamers generated through the S20 method in which the calculated logPtol/w was within 0.01 logPtol/w units of the reported experimental values. Depending on the choice of descriptors, machine learning techniques may or may not capture this subtle electronic effect. As well, when treating the solvent implicitly for QM methods that use a single conformation, this positioning of the lone pairs may not be captured either as QM optimization algorithms focus on finding the nearest local minimum, which may not reflect these subtle chemical changes that can impact the dipole moment. Therefore, if care is not taken to capture these features when generating 3D structures from tools like OpenBabel with SMILES strings, these subtle chemical features can get lost, especially when utilized in larger scale quantum mechanical machine learning workflows. clustering techniques can be used effectively. Refinement and experience with each of these techniques would yield more quantitatively accurate and qualitatively consistent results, but even without such expertise, the accessible methods used herein can readily facilitate thermodynamic property prediction essential for cost-effective rational drug design.