We found several key steps for proper implementation. For the training of the GPs (i.e., hyperparameter optimisation), at least 100 optimisation restarts are needed to consistently find the optimal log likelihood, especially with small data. It is also necessary to constrain the hyperparameters and add priors so they will not train to extremely large or small values early on. Spectral sampling will randomly fail due to problems with the singular value decomposition (SVD). Therefore, we had to implement "retries" in the code to restart the spectral sampling in these cases. Also, 1500 spectral sampling points were needed to get good results with reasonable computational performance. For comparison, the original code even used 4000 spectral points but implemented several portions of the code in C, since the SVD is the most computationally intensive part of TSEMO. Finally, it is important to have a good implementation of NSGA-II; we used pymoo.