SUMMARY The use of seismic-derived structure tensor to guide the inversion of non-seismic data has been gaining increasing attention because of its ability to constrain the inversion result to honour structural information from seismic reflectivity. While the cross-gradient method has been recently adapted to this problem, there is a need to further examine how the choice of the various regularization weighting factors and the design of the starting model impact the image-guided results. In this paper focused on marine magnetotellurics (MT), we study several practical aspects of seismic-guided electromagnetic (EM) inversion to see on what can be improved from the current practice. We then show practical examples of how the cross-gradient method can be applied to get a more geologically pleasing model that fits the data. We also show that there are subjective choices in how to effectively apply the method. First, using a field-realistic synthetic model, we established what would be the suitable regularization weights in the vertical and horizontal directions. Secondly, for the actual 3-D survey data, we compared the use of structure tensors derived from anisotropic pre-stack depth migration (APSDM) reflectivity, high-fidelity full-waveform inversion (FWI) velocity and acoustic impedance inversion volumes to guide cross-gradient anisotropic resistivity inversion with initial half-space resistivity starting models. We determined post-facto the optimum structure tensor weights using well logs as the ground truth. We found that the initial model built using horizon-based interpolated resistivity logs from multiple wells in the anisotropic inversion guided with seismic FWI velocity gave the best match to well logs as compared to the other options. However, given that resistivity logs from multiple wells may not always be available for a robust initial model construction in frontier exploration, we suggest that the structure tensor from APSDM reflectivity data is still preferred since the APSDM reflectivity data have well-defined structural information.