In this paper, we present our participation in the CheckThat! 2025 Task 4b on scientific claim-source retrieval. Our work systematically explores the impact of style transfer on performance in retrieving the scientific publication referenced by a COVID-19-related tweet. We apply seven distinct style transfer methods, distributed across claims and sources, to assess their impact on retrieval performance. These style transfer methods are evaluated across 15 retrieval systems, including 1 sparse, 7 dense, and 7 hybrid models, by testing each system with all combinations of claim and source styles. To guide the style transfer process, we employ a modular zero-shot prompting template with detailed instructions using a large language model (LLM). Our results show that GritLM-7B achieves the best performance without style transfer, suggesting strong robustness to informal text. In contrast, the majority of models, especially sparse and hybrid ones, benefit from applying a formal writing style to claims. We observe that hybrid retrieval models tend to outperform their dense counterparts. This highlights the potential advantage of integrating sparse and dense retrieval paradigms for scientific claim-source retrieval.