We introduce a novel shared task for semantic retrieval from legal texts, where one is expected to perform a so-called contract discovery–extract specified legal clauses from documents given a few examples of similar clauses from other legal acts. The task differs substantially from conventional NLI and legal information extraction shared tasks. Its specification is followed with evaluation of multiple k-NN based solutions within the unified framework proposed for this branch of methods. It is shown that stateof-the-art pre-trained encoders fail to provide satisfactory results on the task proposed, whereas Language Model based solutions perform well, especially when unsupervised fine-tuning is applied. In addition to the ablation studies, the questions regarding relevant text fragments detection accuracy depending on number of examples available were addressed. In addition to dataset and reference results, legal-specialized LMs were made publicly available.