Table shows that the best fine-tuned model achieves around 0.1 -0.2 kcal/mol lower error than the pre-trained model on the unseen experimental reactions. The performance gain is relatively big considering the small size of the fine-tuning set compared to the size of the pre-training set. The fine-tuning set contains bimolecular reactions that the pretraining set lacks and includes more common classes of reactions while the pre-training set largely contains uncommon reactions with high gas phase barrier heights (E a > 50 kcal/mol). Hence, even a relatively small number of fine-tuning data greatly enhances the model's performance on the experimental set, which mostly contains low-barrier reactions and several bimolecular reactions. A similar result was observed in the work by Spiekermann et al. where a model that was initially pre-trained with lower accuracy DFT data showed substantial improvement on barrier height predictions after fine-tuning with a small number of higher accuracy CCSD(T)-F12 data. Both their and our studies demonstrate that different types of data sets can be best leveraged via transfer learning when only a limited amount of higher quality or more relevant data is available. Transfer learning is particularly beneficial for our study since we could avoid mixing the two data sets that differ in the level of theory used for geometry optimizations and also put more emphasis on the data set that is considered to be more relevant to real liquid phase systems. It is also worthwhile to note that the model has similar or slightly lower errors on the pre-training set reaction split after fine-tuning. Even though the pre-training and finetuning sets differ in the level of theory used for geometry optimizations and the types of reactions included, fine-tuning improves the model's performance on the pre-training test set as well. Our prior study 33 demonstrated that the ∆∆G ‡ solv calculations using the COSMO-RS method are not too sensitive to the level of theory used for geometry optimizations for the 15 experimental reactions tested. Similar conclusion can be deduced from the current result as the fine-tuning set, which is based on the M06-2X/cc-pVTZ geometries, still helps or does not exacerbate the model's predictions on the pre-training set, which is based on the ωB97XD3/def2-TZVP geometries. However, the model can have drastically different outcomes depending on the number of fine-tuning epochs used. In this work, the ML model was trained up to the chosen number of maximum epochs, and the final model was selected based on the best validation loss. We used the maximum epoch of 80 for pre-training and used the smaller maximum epoch of 10 for fine-tuning to prevent the pre-trained information from being completely overwritten by the three reaction families used in the fine-tuning set. The fine-tuning epoch was set to 10 because the validation error plateaued out after 10 epochs, as depicted in Figure .