Hi, In the fitmap command global search, how the unique fit is selected from the cluster of similar fits? Ideally, from each cluster, I would like to obtain the fit the gave the best cross-correlation. But, I think, based on the fit_search code in FitMap/search.py, what is happening is that the first unique fit ever encountered is added to the fit list: close = b.close_transforms(ptf) if len(close) == 0: transforms = [M.multiply_matrices(ptf, mtv) for mtv in mtv_list] stats['hits'] = 1 f = Fit(models, transforms, volume, stats) f.ptf = ptf flist.append(f) b.add_transform(ptf) fo[id(ptf)] = f else: s = fo[id(close[0])].stats s['hits'] += 1 and any subsequent fit that is close to the first, would be discarded, even if it gives better cross-correlation. Do I understand correctly that the unique fit is not necessarily the best scoring fit from the cluster? Thanks in advance for clarification, Jan