Does a lexical retriever suffice as large language models (LLMs) become more capable within an agentic search loop? We revisit this question by pairing BM25 with increasingly capable LLMs and introduce Piika, a search agent equipped with three tools for retrieving, browsing, and reading documents. On the BrowseComp-Plus benchmark, under the same GPT-5 + BM25 setup, Piika improves the accuracy from 58.3% to 74.6% while reducing cost from $400 to $95. Despite relying only on lexical retrieval, it also outperforms the previously reported GPT-5 + dense-retriever setup, which achieves 73.0% accuracy at $361. Piika further scales with stronger LLMs, reaching 83.1% answer accuracy and 94.7% surfaced-evidence recall with GPT-5.5. Across model families, our results show that simple lexical retrieval, coupled with capable LLMs, a lightweight harness, and an effective tool interface, can remain highly competitive with more complex retrieval systems. Source code and evaluation artifacts are available at https://piika.ai.