Target-oriented inversion (TOI) is an approach aimed at enhancing the ability of full-waveform inversion (FWI) to achieve a high-resolution delineation of a reservoir. FWI has demonstrated its potential to address the challenge of imaging complex structures on a considerable number of field data applications. Nevertheless, it is still costwise impractical to implement FWI with the full band of seismic data as, in this case, we need to discretize the whole subsurface model space with a fine grid to handle the high frequencies and satisfy the interpretation of, for example, reservoir-scale features. Redatuming techniques enable us to obtain a virtual dataset at the target level from the original data acquisition that is most commonly deployed on the Earth's surface. The virtual dataset can help us apply a high-resolution FWI to the target region, which often occupies a small area of the entire model space. To analysis such a redatuming process, we need to estimate an overburden model that can accurately describe the kinematics and dynamics of the wave propagation. Fortunately, our virtual data retrieval can rely on the overburden estimation with relatively low resolution, since the high-frequency multiple scattering has a limited effect on the deep part and on the corresponding virtual data. Therefore, we start with macro overburden models that contain reasonably accurate kinematics, then apply FWI on the overburden with only low-frequency data. The resulting model is used to implement a least-squares waveform redatuming using the full band. The Marmousi model and Chevron 2014 benchmark dataset are used to demonstrate the effectiveness of our strategy, which results in the high-resolution inversion of the target areas. Our proposed TOI workflow leads to an obvious boost in efficiency and reduces the memory requirement, as the finer grid needed for the high frequencies is only adopted for the redatuming and the TOI.