Artificial Intelligence (AI) methods based on Knowledge Graphs (KGs) have recently been proposed to generate explanations for scientific discovery tasks. Although these approaches offer a promising foundation for accelerating research and development, their practical adoption depends on a clearer understanding of which factors influence the usefulness and trustworthiness of the explanations they generate. We conducted an in-depth user study with eleven biomedical researchers on KG-based explanations for two key scientific discovery tasks in drug development: drug repurposing and drug–target interaction. We evaluated the relevance, completeness, and validity of path-based explanations extracted from KGs by state-of-the-art methods, and compared them along two dimensions: whether they produce single vs. multiple explanatory paths, and whether they incorporate ontological information. The evaluation combined participants’ ratings with a qualitative analysis of feedback to understand how experts interpret these explanations. Our results revealed that biomedical researchers prefer explanations that integrate ontological information and present diverse, biologically plausible mechanisms over those based solely on structural connectivity. These findings shed light on key design choices for KG-based AI systems that aim to support informed candidate selection in drug development and, more broadly, improve their impact in scientific applications.