Obstacles to collaborative research between experimentalists, theorists, and computational researchers
I'm a computational chemist. I've worked with many experimental collaborators with wildly varying experiences. Barriers certainly can and most definitely exist. Here are some of the major ones.


Lack of understanding: This is perhaps the most common and basic reason and results from a lack of experience or knowledge in experimental techniques by computationalists and vice versa, quite simply because they were not trained in this area. As a computationalist, it is natural that I may misunderstand how certain experimental techniques work (especially the state of the art) because I do not spend most of my time working on it. This can result in me over-trusting a piece of experimental evidence, or underestimating the time and effort it can take to conduct the experiments. And believe me, this can definitely happen the other way round. One example, an experimental collaborator asked for some 'quick calculations' in a month and I had to point out that it would easily take a year and all of our computational budget to complete it. 
Politics: In a collaboration, there is always the question of who is the major contributor. Yes, it is possible to have multiple corresponding and first authors, but it rarely changes this fact. In my field (chemistry), usually the experimental side is the major participant of a collaborative work. This means a computational PI will benefit significantly less, and his student will usually not be a first author. This generally leads to conflicts regarding contribution of time and resources given the unequal recognition of work. Sometimes, in order to solve this issue, particularly for long-term collaborations, the experimentalist and computationalist can 'take turns' directing the project and claiming the major contribution to a paper. (I am not supporting or criticizing this phenomenon, but it definitely exists in the field.)
Geographic/temporal reasons: Quite simply, many if not most collaborations occur between groups in different universities and different countries. The simple fact is you cannot communicate them like you would with your group members in the same room. This results in the need to schedule meetings and email communications which are often difficult (professors are busy!) and are invariably an inefficient way of transferring information. I've has cases where several weeks or even months of work were invalidated/or made irrelevant because I was only informed of new experimental findings many months later in a meeting. 
Different objectives/expectations: There is some overlap with the political reason, but sometimes it can be purely due to academic reasons. It can be common for experimental and computational groups to have different expectations for what knowledge/results a work is supposed to yield. For example, a study can be trivially easy for a computationally group (and possibly uninteresting) to accomplish (via modelling), but can be extremely difficult for an experimental group to synthesize/characterize. Alternatively, a relatively simple reaction for an experimental group to conduct can be a multi-year effort by several researchers (or it can even be unsolvable/impractical by current techniques!).


Is it possible to overcome these barriers? Certainly. I've had collaborators who are well aware of these issues (from experience), and end up being very enjoyable to work with. As an aspiring researcher, the best thing you can do is keep these issues in mind when collaborating with other groups. If possible, try to learn the techniques which your collaborators are currently using, and maintain a steady stream of communication with whoever your direct counterpart is (usually, if you are a student, keep in contact with the corresponding student in the other group instead of the professor directly - though you can/should CC him and your advisor to keep them in the loop).

Edit: I decided to add some examples of hypothetical collaborators from the perspective of a computational researcher (for fun). Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

The Good: The well-informed and reasonable collaborator. Knows their stuff (even on your side), up to date on literature, knows what is practical and not. Knows if you are BSing them or not. Won't do you wrong if you do your job. Keep them well-fed with updates. Expect to do good science with them. 

The Bad: The villain/antagonist. Only works with you because you are part of the same grant or project, and only sees you as a way of getting higher impact factor. Only interested in positive results, gives unreasonable deadlines, does not like to hear the phrase "but that can't be done!' Never satisfied, keep him well-fed, but for your own survival.

The Ugly: Nice guy, easy to work with. Okay with whatever you have. Takes anything you offer, but not terribly interested in the science behind the results. Likes you because you can help him get higher impact factor journals. Expect him to disappear one day when he has found someone better or no longer needs your help.