Natural Language Processing is about techniques to automatically extract relevant information from language, primarily text. Recent advancements in NLP take the perspective of representation learning for language, where language is represented by learned vectors in a high dimensional space. In this workshop we will look at how these learned representations of language can be used to search for semantically similar pieces of text. We will look at baselines techniques such as TF-IDF and compare it to contemporary methods using pre-trained Transformers.