Accurately predicting partition coefficients log P is crucial for reducing costs and accelerating drug design as it provides valuable information about the bioavailability, pharmacokinetics, and toxicity of different drug candidates. However, the performance of the existing methods is ambiguous, making it unclear whether these methods can be effectively utilized in drug discovery. To assess the performance of these methods, a series of SAMPL challenges have been conducted over the past few years, aiming to enable the development and validation of predictive models. In this study, we present two independent contributions to the SAMPL9 challenge for predicting the toluene/water partition coefficients for 16 molecules. Both submissions, A and B, use the COSMO-RS approach, albeit in slightly different procedures, to compute the transfer free energies from water to toluene of the molecules presented in the challenge, and consequently, their corresponding log P values. Based on the results, COSMO-RS submission A achieves the top position with an R² value of 0.93 while it ranks second in terms of root-mean-square error (RMSE) with a value of 1.23 log P units. COSMO-RS submission B achieves an R² value of 0.83 and an RMSE value of 1.48 log P units. Following the challenge, we predict the log P values using a neural network model, which was pre-trained on COSMO-RS data achieving an R² of 0.92 and RMSE of 1.04 log P units. Compared to previous SAMPL challenges, all contributions displayed large deviations in predicting the toluene/water partition coefficient. These large deviations emphasize that further research is needed to develop accurate and reliable methods for modeling solvent effects on small molecule transfer-free energies.