Delta learning requires the calculation of low-level activation energies, which involves the challenging task of locating transition state structures. This process is significantly more complex than computing molecular properties for feature engineering, as transition state searches are non-trivial and often have a certain failure rate. This added complexity is analyzed in detail in Figure , which outlines the computational costs associated with each step of molecular property calculations and transition state searches. Moreover, unlike transfer learning, which requires generating low-level training data only during the training phase, delta learning necessitates performing transition state searches for every query of activation energy during model application.