In the current world, individuals are faced with decision making problems and opinion formation processes on a daily basis. For example, debating or choosing between two similar products. However, answering a comparative question by retrieving documents based only on traditional measures (such as TF-IDF and BM25) does not always satisfy the need. Thus, introducing the argumentation aspect in the information retrieval procedure recently gained significant attention. In this paper, we present our participation at the CLEF 2021 Touché Lab for the second shared task, which tackles answering comparative questions based on arguments. Therefore, we propose a novel multi-layer architecture where the argument extraction task is considered as the main engine. Our approach therefore is a pipeline of query expansion, argument identification based on DistilBert model, and sorting the documents by a combination of different ranking criteria.