Large language models (LLMs) are increasingly deployed as web-browsing agents, yet current benchmarks mostly evaluate explicit queries over clean factual evidence. We introduce Melonbench, a 665-item benchmark for coded entity resolution over noisy open-web user-generated content, instantiated with mid-to-long-tail public figures in Chinese entertainment blind-item discourse. Solving Melonbench requires agents to decode obfuscated clues, retrieve fragmented evidence, and identify the target entity. The benchmark is constructed through a five-stage semi-automatic pipeline with atomic event stratification, adversarial faithfulness auditing, and expert verification. Across 30 systems, the best non-search model reaches 25.1% accuracy and the best search-enabled system reaches 49.9%. Controlled comparisons of the same models with and without web search confirm consistent gains from web search, but also substantial remaining headroom. Diagnostic experiments identify semantic de-obfuscation and open-ended entity recall as key bottlenecks, and show that reasoning-centric search can outperform report-oriented agents. An 80-item finance pilot provides preliminary evidence of transfer beyond entertainment within a Chinese-language setting. Melonbench offers a challenging initial testbed for search agents under intentional obfuscation and noisy evidence. The dataset is publicly available at https://huggingface.co/datasets/janehaha/MelonBench.