diff --git a/node/src/main/kotlin/net/corda/node/services/identity/IdentityServiceUtil.kt b/node/src/main/kotlin/net/corda/node/services/identity/IdentityServiceUtil.kt
new file mode 100644
index 0000000000..b5f065cc85
--- /dev/null
+++ b/node/src/main/kotlin/net/corda/node/services/identity/IdentityServiceUtil.kt
@@ -0,0 +1,24 @@
+package net.corda.node.services.identity
+
+import net.corda.core.identity.CordaX500Name
+import net.corda.core.identity.Party
+
+
+fun partiesFromName(query: String, exactMatch: Boolean, x500name: CordaX500Name, results: LinkedHashSet<Party>, party: Party) {
+
+    val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
+    components.forEach { component ->
+        if (exactMatch && component == query) {
+            results += party
+        } else if (!exactMatch) {
+            // We can imagine this being a query over a lucene index in future.
+            //
+            // Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
+            // strings such as entity/company names, and to detect small typos. We can also apply it for city
+            // or any keyword related search in lists of records (not raw text - for raw text we need indexing)
+            // and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
+            if (component.contains(query, ignoreCase = true))
+                results += party
+        }
+    }
+}
\ No newline at end of file
diff --git a/node/src/main/kotlin/net/corda/node/services/identity/InMemoryIdentityService.kt b/node/src/main/kotlin/net/corda/node/services/identity/InMemoryIdentityService.kt
index 92e06f437d..4487128d27 100644
--- a/node/src/main/kotlin/net/corda/node/services/identity/InMemoryIdentityService.kt
+++ b/node/src/main/kotlin/net/corda/node/services/identity/InMemoryIdentityService.kt
@@ -103,22 +103,7 @@ class InMemoryIdentityService(identities: Array<out PartyAndCertificate>,
     override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> {
         val results = LinkedHashSet<Party>()
         for ((x500name, partyAndCertificate) in principalToParties) {
-            val party = partyAndCertificate.party
-            val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
-            components.forEach { component ->
-                if (exactMatch && component == query) {
-                    results += party
-                } else if (!exactMatch) {
-                    // We can imagine this being a query over a lucene index in future.
-                    //
-                    // Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
-                    // strings such as entity/company names, and to detect small typos. We can also apply it for city
-                    // or any keyword related search in lists of records (not raw text - for raw text we need indexing)
-                    // and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
-                    if (component.contains(query, ignoreCase = true))
-                        results += party
-                }
-            }
+            partiesFromName(query, exactMatch, x500name, results, partyAndCertificate.party)
         }
         return results
     }
diff --git a/node/src/main/kotlin/net/corda/node/services/identity/PersistentIdentityService.kt b/node/src/main/kotlin/net/corda/node/services/identity/PersistentIdentityService.kt
index a8ff0525e0..f2e01d2079 100644
--- a/node/src/main/kotlin/net/corda/node/services/identity/PersistentIdentityService.kt
+++ b/node/src/main/kotlin/net/corda/node/services/identity/PersistentIdentityService.kt
@@ -180,22 +180,7 @@ class PersistentIdentityService(override val trustRoot: X509Certificate,
     override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> {
         val results = LinkedHashSet<Party>()
         for ((x500name, partyId) in principalToParties.allPersisted()) {
-            val party = keyToParties[partyId]!!.party
-            val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
-            components.forEach { component ->
-                if (exactMatch && component == query) {
-                    results += party
-                } else if (!exactMatch) {
-                    // We can imagine this being a query over a lucene index in future.
-                    //
-                    // Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
-                    // strings such as entity/company names, and to detect small typos. We can also apply it for city
-                    // or any keyword related search in lists of records (not raw text - for raw text we need indexing)
-                    // and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
-                    if (component.contains(query, ignoreCase = true))
-                        results += party
-                }
-            }
+            partiesFromName(query, exactMatch, x500name, results, keyToParties[partyId]!!.party)
         }
         return results
     }