Large Language Models (LLMs) have the potential to improve healthcare information access in Nigeria, but they risk generating unsafe or inaccurate responses when used in low-resource languages such as Yorùbá. Retrieval-Augmented Generation (RAG) has since emerged as a promising approach to mitigate hallucinations by grounding LLM outputs in verified knowledge sources. To assess its effectiveness in low-resource contexts, we construct a controlled Yorùbá QA dataset derived from Nigerian drug labels, comprising 460 question–answer pairs across 92 drugs, which was used to evaluate the impact of different retrieval strategies: hybrid lexical–semantic retrieval, Hypothetical Document Embeddings(HyDE), and Cross-Encoder re-ranking. Our results show that hybrid retrieval strategies, combining lexical and semantic signals, generally yield more reliable and clinically accurate responses, while other advanced re-ranking approaches show inconsistent improvements. These findings hereby underscore the importance of effective retrieval design for safe and trustworthy multilingual healthcare QA systems.