The increasing number of Chemical Compounds is a challenge for the researchers to explore such datasets. In this work, we propose the use of Recommender Systems in the exploration of new Chemical Compounds of interest to scientific researchers. Our approach consists in a Hybrid recommender model suitable for implicit feedback datasets and focused in retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than 10 percentage points in most of the assessed evaluation metrics.