The Ophiuchus cloud complex is one of the best laboratories to study the earlier stages of the stellar and protoplanetary disc evolution. We constructed a control sample composed of 188 bona fide Ophiuchus members. Using this sample as a reference we applied three different density-based machine learning clustering algorithms (DBSCAN, OPTICS, and HDBSCAN) to a sample drawn from the Gaia DR2 catalogue centred on the Ophiuchus cloud that contains 2300 sources covering a sky area of 38deg^2^. The clustering analysis was applied in the five astrometric dimensions defined by the three-dimensional Cartesian space and the proper motions in right ascension and declination. The three clustering algorithms systematically identify a similar set of candidate members in a main cluster with astrometric properties consistent with those of the control sample. We constructed a common sample containing 391 member candidates including 166 new objects, which have not yet been discussed in the literature. We built the SEDs for a subset of 48 objects and found a total of 41 discs.