We introduce FormBench, a platform for testing the ability of models to retrieve precise chemical information from technical documents. FormBench contains 55,352 queries and two retrieval corpus variants each with ~1M passages: FormBench-Random (C0), with randomly sampled distractors, and FormBench-Structured (C1), with structurally proximate distractors. Each query is paired with an anchor passage, a hard negative, and a soft negative - derived from a materials ontology through entity overlap scoring, providing a deeper graded signal on a model's discriminatory power. We evaluate 10 model configurations spanning commercial API encoders (OpenAI, Voyage), open-source encoders (Nomic, GTE-large, Qwen3-Embed), and their task-adaptive fine-tuned (TAPT) variants. We find: (1) commercial APIs score very poorly across all configurations (OpenAI-3-large: 0.009 on C1, 0.213 on C0); (2) TAPT on FormBench triplets lifts Qwen3-Embed to 0.4085 on C1, 0.4835 on C0; (3) BM25 remains competitive (0.375 on C1, 0.467 on C0), exceeded only by TAPT-Qwen3; (4) corpus size has a significant impact on retrieval behavior, as demonstrated through a corpus-size ablation; (5) the API failure traces to within-domain geometric collapse; (6) APIs outperform even the TAPT variants on classification and clustering tasks. FormBench is released in BEIR-compatible format at huggingface.co/datasets/vinven7/FormBench under CC-BY-4.0.