Abstract Knowledge graphs are powerful tools for representing and organising complex biomedical data. They empower researchers, physicians, and scientists by facilitating rapid access to biomedical information, enabling the discernment of patterns or insights, and fostering the formulation of decisions and the generation of novel knowledge. To automate these activities, several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, the efficacy of these embedding algorithms appears limited when applied to biomedical knowledge graphs, prompting questions about whether they can be useful in this field. To that end, we explore several widely used knowledge graph embedding models and evaluate their performance and applications using a recent biomedical knowledge graph, BioKG. We also demonstrate that by using recent best practices for training knowledge graph embeddings, it is possible to improve performance over BioKG. Additionally, we address interpretability concerns that naturally arise with such machine learning methods. In particular, we examine rule-based methods that aim to address these concerns by making interpretable predictions using learned rules, achieving comparable performance. Finally, we discuss a realistic use case where a pretrained BioKG embedding is further trained for a specific task, in this case, four polypharmacy scenarios where the goal is to predict missing links or entities in another downstream knowledge graphs in four polypharmacy scenarios. We conclude that in the right scenarios, biomedical knowledge graph embeddings can be effective and useful. Availability and implementation Our code is available at https://github.com/aryopg/biokge.