Autonomous exploration of unknown environments usually focuses on maximizing the volumetric exploration of the surroundings. Object-oriented exploration, on the other hand, tries to minimize the time spent on the localization of some given objects of interest. While the former problem equally considers map growths in any free direction, the latter fosters exploration towards objects of interest partially seen and not yet accurately identified.The proposed work relates to a novel algorithm that focuses on an object-oriented exploration of unknown environments for aerial robots, able to generate volumetric representations of surroundings, semantically enhanced by labels for each object of interest.As a case study, this method is applied both in a simulated environment and in real-life experiments on a small aerial platform.