Commercial contract clauses exhibit substantial linguistic variation and semantic overlap, making fine-grained classification challenging for small language models (SLMs). We investigate whether retrieving semantically similar labeled clauses can provide task-specific evidence at inference time and improve classification without modifying the underlying SLM. Using the Contract Understanding Atticus Dataset (CUAD), we formulate clause classification as a retrieval-conditioned prediction task in which relevant training clauses are retrieved and provided as in-context demonstrations to a locally deployed SLM. We evaluate prompting-only, retrieval-only, and retrieval-conditioned configurations on the same contract-disjoint held-out set of 1,679 clauses. The proposed retrieval-conditioned SLM achieves 66.41% accuracy and 51.72% macro-F1, compared with 35.80% and 31.41% for the zero-shot SLM and 50.98% and 40.93% for the random few-shot SLM, respectively. Retrieval-only nearest-neighbor baselines achieve 47.48–47.82% macro-F1, while conditioning the SLM on the same retrieved evidence increases macro-F1 to 51.72%. These results show that retrieval provides substantial task-specific context and that SLM conditioning provides additional improvement beyond direct nearest-neighbor label assignment, without task-specific parameter fine-tuning. The findings support retrieval-conditioned SLMs as a promising direction for evidence-grounded and locally deployable legal NLP