The feature importances that come out of RF are relative to the forest it built, regardless of the model performance. They are also relative to each other. It is quite possible that your data do not support a predictive model very well, but RF will still compute their relative effects and give importances. I would first try tweaking the RF parameters (minimum per node, minimum to split, maximum depth, etc.) to get a good model. You can also remove the less important variables to see if RF performance improves.
You should also run the data through another method, perhaps SVM, and see if you get good prediction. If another model can give you decent prediction, then go back to your RF importances, cut out the low end, and rebuild your SVM model.
In general, you always want to validate model output by another method if you can.
You mention "feature interactions", but feature importances won't give you that. You need to create dummy features that represent any interactions you want to look at and see if RF evaluates them with high importance.