We empirically investigated the SR-GINE approach's scalability by comparing its training time and performance to the non-extended GINE architecture. The average runtime for training GINE-SR on the OCELOT chromophore data set (N train = 20201) is 9m44s±44s, which is an increase of 6.2% compared to GINE (9m10s±42s). On the larger QM9 data set (N train = 107108), the training time increases to 50m25s±0m47s for GINE and 62m25s±3m27s for GINE-SR, an increase of 23.8%. In the case of QM9, this increase in training time is close to the average increase in performance, which is 24.4% (Table ). Although these numbers suggest that SR-GINE can scale well in terms of a performance-training time trade-off, it is not guaranteed that this is the case for all data sets. However, our experiments throughout this study showed a substantial increase of performance of SR-GINE over GINE on small to medium-sized data sets where the increase in training time was marginal as shown with the example of OCELOT. Overall, these observations agree with the evaluation of RepSet by Skianis et al. [16]. The timing data was taken form training runs on a Nvidia RTX 4090Ti GPU with 12 GB RAM for GINE and SR-GINE.