Finding similar documents in a big document corpus based on context has many practical applications especially in the legal sector. In this paper, our focus is on the documents related to environmental law which have been collected in a database of approximately 300k documents. We analyzed the performance of different representation models (called document embeddings) on our database and found that evaluating the results is difficult, due to the size of the database. The approaches presented can be applicable for other text datasets.