We report on our submission to Task 1 of the GermEval 2021 challenge -- toxic comment classification. We investigate different ways of bolstering scarce training data to improve off-the-shelf model performance on a toxic comment classification task. To help address the limitations of a small dataset, we use data synthetically generated by a German GPT-2 model. The use of synthetic data has only recently been taking off as a possible solution to ad- dressing training data sparseness in NLP, and initial results are promising. However, our model did not see measurable improvement through the use of synthetic data. We discuss possible reasons for this finding and explore future works in the field.