Do I need to include all possible three-way interactions in a mixed-effects model?

I'm building a mixed-effects model to investigate how several explanatory variables affect an outcome. Based on my exploratory data analysis, I suspect that certain combinations of variables have three-way interactions. For example, suppose I have four explanatory variables: A, B, C, and D. I suspect there is a three-way interaction between A × B × C, but I haven't found clear evidence suggesting interactions involving D. My main question is: If I include a three-way interaction between A, B, and C (A × B × C) in my model, do I also need to consider other possible three-way interactions, such as A × B × D, A × C × D, and B × C × D? Or is it reasonable to include only interactions that appear plausible based on exploratory analysis? The experiment is still exploratory, and, to my knowledge, there is little or no existing literature to guide my expectations about which variables are likely to interact. I'm therefore concerned that selecting interactions based on exploratory findings alone could introduce bias, but including every possible interaction could make the model unnecessarily complex. At the same time, I don't want to overlook potentially important interactions simply because they weren't apparent during the exploratory analysis. I'd also appreciate advice on the statistical implications of selecting interaction terms based on exploratory analysis, as I'm concerned that including only interactions that appear significant or interesting in the data might constitute che…