Repository-level code completion remains challenging for large language models (LLMs), as it requires reasoning over cross-file dependencies while under limited context windows. To address this challenge, prior work has adopted Retrieval-Augmented Generation (RAG) frameworks based on semantic indexing or structure-aware graph analysis. Although effective, these approaches introduce substantial computational overhead for index construction and maintenance, which hinders their practicality in real-world development. Motivated by common developer workflows that rely on lightweight search utilities (e.g., ripgrep) to locate relevant code, we revisit a fundamental yet underexplored question: how far can simple, index-free lexical retrieval go in supporting repository-level code completion before more complex retrieval mechanisms become necessary? To answer this question, we systematically explore the potential of lightweight, index-free, intent-aware lexical retrieval through extensive empirical analysis. We first introduce Naive GrepRAG, a baseline framework where LLMs autonomously generate ripgrep commands to localize relevant context. Our preliminary experiments show that even this basic implementation achieves performance comparable to sophisticated graph-based baselines. Further analysis reveals that its effectiveness stems from retrieving code fragments that are lexically precise and spatially closer to the completion site. However, we identify key limitations of this approach, including sensitivity to noisy matches caused by high-frequency ambiguous keywords and context fragmentation due to rigid truncation boundaries. To address these issues, we propose GrepRAG, which augments lexical retrieval with a lightweight post-processing pipeline featuring identifier-weighted re-ranking and structure-aware deduplication. Extensive evaluation on CrossCodeEval and RepoEval_Updated demonstrates that GrepRAG consistently outperforms state-of-the-art (SOTA) methods. In particular, on CrossCodeEval, GrepRAG achieves 7.04–15.58% relative improvement in code exact match (EM) over the best baseline.