Transfer learning also performs well across various chemical classes but shows higher variability in classes such as benzene derivatives and amides. This variability suggests that while transfer learning can improve model accuracy by integrating different data types, it may still face challenges in fully capturing the intricate properties of more complex molecules. For instance, the errors are more pronounced in the benzene derivatives class in the EXT-Zamora dataset, indicating a potential limitation in handling aromatic systems. This might be due to the fact that not enough data are available for the fine-tuning step, as have shown that transfer learning can achieve a great agreement between predictions and experiments if enough high fidelity data are available.