diff --git a/decoupler/utils.py b/decoupler/utils.py
index d6a9c9e..932ada9 100644
--- a/decoupler/utils.py
+++ b/decoupler/utils.py
@@ -560,39 +560,3 @@ def read_gmt(path):
     df = pd.DataFrame(df, columns=['source', 'target'])
 
     return df
-
-def annotate_enrichment(enrichment, net, queried_gene_list):
-    """
-    Adds n_genes, lead_genes and lead_genes_count
-    to an enrichment result, provided it includes a geneset field.
-    
-    Parameters
-    ----------
-    queried_gene_list : list of genes originally queried
-    
-    enrichment : data.frame
-        Pandas data frame produced by one of the enrichment methods
-        
-    net : data.frame
-        Pandas data frame with the net used for the above enrichment. 
-        It should contain geneset and genesymbol fields
-        
-    Returns
-    -------
-    df : DataFrame
-        Melted data frame based on enrichment, with additional fields
-        `n_genes`, `lead_genes`, `lead_genes_count`.
-        
-    """
-    qgl = queried_gene_list
-    # melt the enrichment dataframe
-    enrichment_melted = pd.melt(enrichment, value_vars=enrichment.columns, var_name='geneset', value_name='p-value')
-    # add a column with the number of genes in each geneset
-    enrichment_melted['n_genes'] = enrichment_melted['geneset'].map(net.groupby('geneset')['genesymbol'].count())
-    # add a column with the number of lead genes in high_exp that belong to that msigdb geneset
-    enrichment_melted['lead_genes'] = enrichment_melted['geneset'].map(lambda x: ", ".join(set(net[net['geneset'] == x].genesymbol.unique().tolist()).intersection(set(qgl))) )
-    # add a column with the lead genes in high_exp that belong to that msigdb geneset
-    enrichment_melted['lead_genes_count'] = enrichment_melted['geneset'].map(lambda x: len(set(net[net['geneset'] == x].genesymbol.unique().tolist()).intersection(set(qgl))))
-    return enrichment_melted
-    
-