For decades, the success of the similarity search has been based on a detailed quantification of pairwise similarity of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations more time-consuming. While the k nearest neighbours (kNN) search dominates the real-life applications, we claim that it is principally free of a need for precise similarity quantifications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects q,o1,o2 from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of o1,o2 – the decision can also contain a neutral option. We formalise such searching and discuss its advantages concerning similarity quantifications, namely its efficiency and robustness. We also propose a pioneering implementation of the relational similarity search for the Euclidean spaces and report its extreme filtering power in comparison with 3 contemporary techniques.