The documentation of a care episode consists of clinical notes concerning patient care, concluded with a discharge summary. Care episodes are stored electronically and used throughout the health care sector by patients, administrators and professionals from different areas, primarily for clinical purposes, but also for secondary purposes such as decision support and research. A common use case is, given a -possibly unfinished -care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and a semantic neural network model called word2vec. A novel method is introduced that utilizes the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on an experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance by exploiting (1) ICD-10 codes of care episodes and (2) semantic similarity between their discharge summaries. Results suggest that several of the methods proposed outperform a state-ofthe art search engine (Lucene) on the retrieval task.