The goal of this manuscript is to survey various types of combinations that are done in Chemoinformatics. In light of the current rise of machine learning (ML), we also comment on emerging combinations that are being developed, paving the way for original and improved research areas. In addition, in this review, we provide the reference to the literature and/or link to the code when the tools are freely available. As discussed, the combination of research methods can be particularly interesting, as an alternative to single conventional strategies in which the research objectives are seen from a unique perspective. The manuscript is organized into four main sections. After this Introduction, section two discusses sub-disciplines that have emerged or are evolving as the combination of more traditional or longestablished disciplines. Section three presents exemplary types of combinations done involving chemoinformatics with different applications in molecular representation, property prediction, structureproperty (activity) relationships -SP(A)R-, virtual screening, chemical space, ML, and other applications such as chemistry and art. This section does not include all hybrid methodologies but most representative ones around the world were selected. Section four presents summary conclusions.