Sound hybridization is a technique that combines the characteristics of multiple sounds to generate new sonic textures, with applications ranging from scientific analysis to timbral synthesis and assisted musical composition. In this work, we propose a matching-pursuit-based approach for adaptive sound hybridization, which avoids a static segmentation of the target and enables a more flexible and coherent reconstruction. The method iteratively selects sound atoms from a reference database, optimizing the synthesis process in terms of both timbral and temporal-structural coherence. We demonstrate that our approach preserves the structure of the target while introducing controlled variations and analyze the impact of different feature spaces on the quality of the hybridization. The results show that the proposed strategy effectively balances fidelity to the target with an emphasis on the distinctive characteristics of the search space from which the sound objects involved in the hybridization process are selected. Our method prioritizes the aesthetic and creative potential of sound hybridization, offering a tool for artistic exploration.