Background. Inferring an evolutionary scenario for a gene family is a fundamental prob- lem both in functional and evolutionary genomics. The gene tree/species tree reconciliation approach has been widely used to address this problem, but mostly in a parsimony framework, that considers only the reconciliation that minimizes the number of duplication and/or loss events. Recently a probabilistic approach has been developed, based on the classical birth-death process, including efficient algorithms for computing posterior probabilities of reconciliations and orthology prediction. Results. We recently proposed an efficient algorithm for exploring the whole space of gene tree/species tree reconciliations, that we adapt here to compute efficiently, either exactly or approximately depend- ing on the space size, the posterior probability of the visited reconciliations. We use this algorithm to analyze the probabilistic landscape of the space of reconciliations for, both, a real dataset of fungal gene families and simulated data. Conclusion. Our results suggest that with realistic gene duplication and loss rates, a very small subset of all reconciliations needs to be explored in order to approximate very closely the posterior probability of the most likely reconciliations. For cases where the posterior probability mass is more evenly dispersed, our method allows to explore efficiently the required subspace of reconciliations.