In conclusion, we have explored the robustness of GNN potentials based on the recent GemNet architecture in MD simulations. We find that sufficiently large training sets are key to obtaining robust GNN potentials and that a low test set error does not guarantee that stable trajectories can be generated. Interestingly, in some cases severe instabilities were only discovered after hundreds of ps of dynamics. The test set error should thus not be taken at face value as a measure for the error one can expect in 'real' applications. Demonstrating 'chemical accuracy' on a test set is by itself not enough.