The active global SARS-CoV-2 pandemic caused more than 167 million cases and 3.4 million deaths worldwide. As mentioned by Ye et al.(2021), the development of completely new drugs for such a novel disease is a challenging, time intensive process and despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments found among existing drugs for meant different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi & Chepuri (2020) originally presented the model DR-COVID. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of candidate drugs, 32of which currently being in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the model by Doshi & Chepuri (2020) by significantly shortening the inference and pre-processing time by exploiting data-parallelism.