With the rise of deep learning, artificial neural networks (ANNs) have been applied to predict kinetic parameters, with the multi-layer perceptron (MLP) approach demonstrating success in various reaction systems, including aqueous OH reactions, OH radical reactions, and tropospheric ozone degradation. However, these methods typically rely on user-defined molecular descriptors, limiting their generalizability across different reaction types. More advanced deep learning architectures, such as those based on graph representations, SMILES strings, and 3D molecular structures, have been developed to automatically learn implicit information from molecular connectivity, often outperforming traditional ANN models. For example, Grambow et al. expanded the applicability of kinetic predictions using a template-free graphbased model, employing dual directed-message passing neural network (D-MPNN) to encode reactant and product graphs for activation energy prediction. Despite the considerable success of machine learning (ML) methods in predicting molecular properties, there remain significant challenges, particularly concerning data scarcity. High-quality kinetic parameters are often limited, necessitating the development of techniques that enhance model performance without relying on extensive datasets. One such approach involves incorporating additional data computed using lower-level theories to augment the training set. However, identifying which data to compute and how to effectively integrate it into models remains an area of active research. Feature engineering is one strategy for addressing these challenges. By calculating properties of reactants and products relevant to their kinetic behavior using lower-level methods, such as semiempirical quantum mechanical (SQM) techniques, these features can be incorporated into the model to improve performance. For example, Marques et al. demonstrated accuracy improvements by integrating electronic structure information, while Choi et al. utilized molecular structure and thermodynamic properties to predict gas-phase reaction activation energies. Another promising approach is transfer learning, where a model is pre-trained on a large dataset before being fine-tuned on a smaller, target-specific dataset to improve model performances. In the context of activation energy predictions, models can be initially trained on extensive datasets derived from low-level calculations and subsequently fine-tuned using a smaller set of high-quality data, thereby reducing the computational cost of data generation. Previous studies, such as those by Spiekermann et al. and Espley et al., have explored the benefits of transferring models trained at lower theoretical levels to higher-level data, with notable improvements in prediction performance. A relatively novel approach, delta learning, has also emerged as a viable strategy. Delta learning involves predicting the difference between low-level and high-level computed data, allowing the model to learn corrections to low-level computations. This approach has demonstrated promising results, as seen in studies where delta learning was applied to graph neural network (GNN) models to predict high-level data from lower-level calculations. Building on these prior studies, our research aims to systematically investigate and compare the effectiveness of feature engineering, transfer learning, and delta learning applied to GNNs in predicting high-level CCSD(T)-F12a activation energies. These predictions are enhanced by augmenting datasets with low-cost, low-level SQM calculations, as illustrated in Figure . Our objective is to identify the most relevant properties to compute and the optimal methods for integrating these into the model. For feature engineering, we focus on identifying features that significantly enhance the prediction of activation energies, conducting experiments on standardized benchmark datasets to evaluate the effectiveness of additional features compared to alternative methodologies. In transfer learning, we examine the impact of different pre-training domains and data quantities on model performance, comparing results with other methods to assess transfer learning's efficacy in our specific context. Additionally, we evaluate the performance of delta learning within our benchmark framework, aiming to provide a comprehensive comparison of these three approaches in enhancing model accuracy across various reaction types.